Weighted Bayesian Belief Network for diabetics: a predictive model

Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the "diabetes capital of the world" owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring

The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs' health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs' health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs' health and welfare.

Decoding the Possible Molecular Mechanisms in Pediatric Wilms Tumor and Rhabdoid Tumor of the Kidney through Machine Learning Approaches

Objective: Wilms tumor (WT) and Rhabdoid tumor (RT) are pediatric renal tumors and their differentiation is based on histopathological and molecular analysis. The present study aimed to introduce the panels of mRNAs and microRNAs involved in the pathogenesis of these cancers using deep learning algorithms. Methods: Filter, graph, and association rule mining algorithms were applied to the mRNAs/microRNAs data. Results: Candidate miRNAs and mRNAs with high accuracy (AUC: 97%/93% and 94%/97%, respectively) could differentiate the WT and RT classes in training and test data. Let-7a-2 and C19orf24 were identified in the WT, while miR-199b and RP1-3E10.2 were detected in the RT by analysis of Association Rule Mining. Conclusion: The application of the machine learning methods could identify mRNA/miRNA patterns to discriminate WT from RT. The identified miRNAs/mRNAs panels could offer novel insights into the underlying molecular mechanisms that are responsible for the initiation and development of these cancers. They may provide further insight into the pathogenesis, prognosis, diagnosis, and molecular-targeted therapy in pediatric renal tumors.

Risk and Protective Factors of Depression in Family and School Domains for Chinese Early Adolescents: An Association Rule Mining Approach

Depression is one of the most common psychological problems in adolescence. Familial and school-related factors are closely related to adolescents' depression, but their combined effects need further examination. The purpose of this study was to explore the combined effects of risk/protective factors of depression in family and school domains using a sample of Chinese adolescents differing in gender, age group and left-behind status. A total of 2455 Chinese students in primary and secondary school participated in the cross-sectional survey and reported multiple risk/protective factors in family and school environments and depressive symptoms. Association rule mining, a machine learning method, was used in the data analyses to identify the correlation between risk/protective factor combinations and depression. We found that (1) Family cohesion, family conflict, peer support, and teacher support emerged as the strongest factors associated with adolescent depression; (2) The combination of these aforementioned factors further strengthened their association with depression; (3) Female gender, middle school students, and family socioeconomic disadvantages attenuated the protective effects of positive relational factors while exacerbating the deleterious effects of negative relational factors; (4) For individuals at risk, lack of mental health education resources at school intensified the negative impact; (5) The risk and protective factors of depression varied according to gender, age stage and left-behind status. In conclusion, the findings shed light on the identification of high-risk adolescents for depression and underscore the importance of tailored programs targeting specific subgroups based on gender, age, or left-behind status.

Discovering associations between radiological features and COVID-19 patients' deterioration

Background and Aims

Data mining methods are effective and well-known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID-19 by applying the rule mining method using characteristics of medical images.

Methods

This retrospective study has analyzed the radiological data from 104 COVID-19 hospitalized patients diagnosed with COVID-19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes.

Results

Ten rules were extracted with only X-ray-related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan-related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13.

Conclusion

This study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID-19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.

A novel FCTF evaluation and prediction model for food efficacy based on association rule mining

Introduction

Food-components-target-function (FCTF) is an evaluation and prediction model based on association rule mining (ARM) and network interaction analysis, which is an innovative exploration of interdisciplinary integration in the food field.

Methods

Using the components as the basis, the targets and functions are comprehensively explored in various databases and platforms under the guidance of the ARM concept. The focused active components, key targets and preferred efficacy are then analyzed by different interaction calculations. The FCTF model is particularly suitable for preliminary studies of medicinal plants in remote and poor areas.

Results

The FCTF model of the local medicinal food Laoxianghuang focuses on the efficacy of digestive system cancers and neurological diseases, with key targets ACE, PTGS2, CYP2C19 and corresponding active components citronellal, trans-nerolidol, linalool, geraniol, α-terpineol, cadinene and α-pinene.

Discussion

Centuries of traditional experience point to the efficacy of Laoxianghuang in alleviating digestive disorders, and our established FCTF model of Laoxianghuang not only demonstrates this but also extends to its possible adjunctive efficacy in neurological diseases, which deserves later exploration. The FCTF model is based on the main line of components to target and efficacy and optimizes the research level from different dimensions and aspects of interaction analysis, hoping to make some contribution to the future development of the food discipline.

Efficacy and Key Materials of East Asian Herbal Medicine Combined with Conventional Medicine on Inflammatory Skin Lesion in Patients with Psoriasis Vulgaris: A Meta-Analysis, Integrated Data Mining, and Network Pharmacology

Psoriasis is a chronic inflammatory disease that places a great burden on both individuals and society. The use of East Asian herbal medicine (EAHM) in combination with conventional medications is emerging as an effective strategy to control the complex immune-mediated inflammation of this disease from an integrative medicine (IM) perspective. The safety and efficacy of IM compared to conventional medicine (CM) were evaluated by collecting randomized controlled trial literature from ten multinational research databases. We then searched for important key materials based on integrated drug data mining. Network pharmacology analysis was performed to predict the mechanism of the anti-inflammatory effect. Data from 126 randomized clinical trials involving 11,139 patients were used. Compared with CM, IM using EAHM showed significant improvement in the Psoriasis Area Severity Index (PASI) 60 (RR: 1.4280; 95% CI: 1.3783-1.4794; p < 0.0001), PASI score (MD: -3.3544; 95% CI: -3.7608 to -2.9481; p < 0.0001), inflammatory skin lesion outcome, quality of life, serum inflammatory indicators, and safety index of psoriasis. Through integrated data mining of intervention data, we identified four herbs that were considered to be representative of the overall clinical effects of IM: Rehmannia glutinosa (Gaertn.) DC., Isatis tinctoria subsp. athoa (Boiss.) Papan., Paeonia × suffruticosa Andrews, and Scrophularia ningpoensis Hemsl. They were found to have mechanisms to inhibit pathological keratinocyte proliferation and immune-mediated inflammation, which are major pathologies of psoriasis, through multiple pharmacological actions on 19 gene targets and 8 pathways in network pharmacology analysis. However, the quality of the clinical trial design and pharmaceutical quality control data included in this study is still not optimal; therefore, more high-quality clinical and non-clinical studies are needed to firmly validate the information explored in this study. This study is informative in that it presents a focused hypothesis and methodology for the value and direction of such follow-up studies.

Complex, Co-occurring Needs Patterns and Evidence-Based Service Planning for Families Involved in Foster Care: A Map for Research and Practice

This study described the complexity of service need co-occurrence among foster care-involved families and identified prevalent patterns of needs to inform future evidence-based service planning research. We utilized state administrative child maltreatment records, and restricted data to cases where the child entered foster care in 2019 and the caseworker indicated the presence of at least one need from the Family Assessment of Needs and Strengths (FANS; n = 1631). We extracted all unique combinations of needs (i.e., needs profiles), and we used association rule mining to identify patterns within these profiles. A total of 780 unique needs profiles emerged among the 1631 cases, which we condensed into 78 patterns. Although the variability and complexity of needs profiles makes evidence-based service planning difficult, the present analysis mapped prevalent needs patterns to guide future research intended to assist caseworkers in this task. Identification of maltreatment determinants among families involved in foster care, and future research into the needs within different needs patterns that might undermine treatment effectiveness, may result in a better balance between parsimonious service plans and a full consideration of co-occurring service needs.

Ontology is what makes data interesting: Interestingness framework for COVID-19 corpora

The COVID-19 pandemic has already shown to be a worldwide threat, demonstrating how susceptible humans may be. It has also inspired experts from a range of aspects and countries to find the potential solution to control the widespread. In line with this, our research proposes a novel framework for finding interesting facts from COVID-19 corpora using domain ontology. Since data mining with domain knowledge provides semantically rich facts, we use ontology in our proposed approaches. Most of the state-of-the-art methods rely on instance level or user intervention. These methods do not entirely exploit the richness of ontology. In this work, we demonstrate how to extract exciting rules from data at ontology’s schema and instance levels. Our experiments were carried out on two COVID-19 corpora that depict COVID-19 patients’ symptoms and drug information. The proposed framework outperformed the traditional methods by reducing the number of rules by 70% and generating semantic-rich rules that are more user-readable and quickly adopted by decision-makers. Furthermore, to support our claims, we compared the outcomes of the proposed framework with the most recent approach in the field. Also, statistically significant tests and domain expert evaluations are conducted to validate our framework.

The Association Between Comorbidities and Prescribed Drugs in Patients With Suspected Obstructive Sleep Apnea: Inductive Rule Learning Approach

Collapse

Acupuncture for Major Depressive Disorder: A Data Mining-Based Literature Study

Introduction

Acupuncture has a long history of treating major depressive disorder (MDD), yet the acupoint selection of acupuncture for MDD varies greatly. This study aimed to explore the characteristics and principles of acupuncture for MDD by analyzing clinical trials of acupuncture for MDD using data mining techniques.

Methods

In this study, clinical trials of acupuncture for MDD were retrieved and relevant data were extracted, and then the data were analyzed by data mining techniques. In addition, association rule mining, network analysis and hierarchical cluster analysis were used to determine the correlation between different acupoints.

Results

The results revealed that GV20, LR3, PC6, SP6 and GV29 were used most frequently; acupoints in the Yang meridian were used more often than those in the Yin meridian, with the most applied acupoints in the Governor Vessel; the percentage of specific acupoints applied was 69.39%, with the most applied being five-shu points; the frequency of acupoints used was highest in the lower limbs, while the head, face, and neck had the most acupoints used; GV29 combined with GV20 were the most used acupoint groups; the core acupoints used for MDD were GV20, PC6 and SP36; there were 5 acupoint groups according to the cluster analysis. The most used acupuncture method was manual acupuncture; the frequency of treatment was mostly 7 times per week and the duration of treatment was mostly 42 days.

Discussion

We discussed the current character of acupuncture treatment on MDD, including the frequency used of acupoints, the property of used acupoints, the acupoint combination, the acupuncture method, and the frequency and duration of treatment. These findings may provide new ideas for the clinical treatment of MDD. However, further clinical/experimental studies are needed to demonstrate the significance of this concept and approach.

Automatically detecting activities of daily living from in-home sensors as indicators of routine behaviour in an older population

Objective

The NEX project has developed an integrated Internet of Things (IoT) system coupled with data analytics to offer unobtrusive health and wellness monitoring supporting older adults living independently at home. Monitoring involves visualising a set of automatically detected activities of daily living (ADLs) for each participant. ADL detection allows the incorporation of additional participants whose ADLs are detected without system re-training.

Methods

Following a user needs and requirements study involving 426 participants, a pilot trial and a friendly trial of the deployment, an action research cycle (ARC) trial was completed. This involved 23 participants over a 10-week period each with ∼ 20 IoT sensors in their homes. During the ARC trial, participants took part in two data-informed briefings which presented visualisations of their own in-home activities. The briefings also gathered training data on the accuracy of detected activities. Association rule mining was used on the combination of data from sensors and participant feedback to improve the automatic ADL detection.

Results

Association rule mining was used to detect a range of ADLs for each participant independently of others and then used to detect ADLs across participants using a single set of rules for each ADL. This allows additional participants to be added without the necessity of them providing training data.

Conclusions

Additional participants can be added to the NEX system without the necessity to re-train the system for automatic detection of their ADLs.

Systematic mining of patterns of polysubstance use in a nationwide population survey

OBJECTIVES

To identify patterns of association and transition in polysubstance use based on National Survey of Drug Use and Health (NSDUH) in the United States.

METHODS

We developed a new computational platform for PolySubstance Use data Mining for Associations and Transitions (PSUMAnT). It is based on the computation of weighted support, a measure of popularity, for the use of every combination of one or more substances, termed as a drugset, over a period of 5 decades (1965-2014) based on NSDUH data. It uses an efficient bitstring representation with exact and approximate string matching capabilities to search for patterns of association between drugsets and demographics of user groups at different time-intervals. Moreover, it introduces a quantitative definition of a rule of transition between pairs of substances used within a given time-interval, and provides a function for mining them.

RESULTS

We identified the frequent drugsets from individual substance use database, and determined their representation among different demographic groups at different intervals. An interesting pattern of use of pain relievers and tranquilizers was detected for the age-group of 26-34 years. In addition, transition rules for heroin use in the last decade (2004-2015) of the given data were mined.

CONCLUSIONS

Computation of weighted supports over time for every possible combination of substances in the survey, and their association with specific user groups, allows PSUMAnT to generate and test novel, interesting hypotheses in polysubstance use. PSUMAnT can be used for mining combinations of substances used among diverse demographic groups including those that have received less attention in this problem.

Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records

BACKGROUND

Multimorbidity has become a new challenge for medical systems and public health policy. Understanding the patterns of and associations among multimorbid conditions should be given priority. It may assist with the early detection of multimorbidity and thus improve quality of life in older adults.

OBJECTIVE

This study aims to comprehensively analyze and compare associations among multimorbid conditions by age and sex in a large number of middle-aged and older Chinese adults.

METHODS

Data from the home pages of inpatient medical records in the Shenzhen National Health Information Platform were evaluated. From January 1, 2017, to December 31, 2018, inpatients aged 50 years and older who had been diagnosed with at least one of 40 conditions were included in this study. Their demographic characteristics (age and sex) and inpatient diagnoses were extracted. Association rule mining, Chi-square tests, and decision tree analyses were combined to identify associations between multiple chronic conditions.

RESULTS

In total, 306,264 hospitalized cases with available information on related chronic conditions were included in this study. The prevalence of multimorbidity in the overall population was 76.46%. The combined results of the 3 analyses showed that, in patients aged 50 years to 64 years, lipoprotein metabolism disorder tended to be comorbid with multiple chronic conditions. Gout and lipoprotein metabolism disorder had the strongest association. Among patients aged 65 years or older, there were strong associations between cerebrovascular disease, heart disease, lipoprotein metabolism disorder, and peripheral vascular disease. The strongest associations were observed between senile cataract and glaucoma in men and women. In particular, the association between osteoporosis and malignant tumor was only observed in middle-aged and older men, while the association between anemia and chronic kidney disease was only observed in older women.

CONCLUSIONS

Multimorbidity was prevalent among middle-aged and older Chinese individuals. The results of this comprehensive analysis of 4 age-sex subgroups suggested that associations between particular conditions within the sex and age groups occurred more frequently than expected by random chance. This provides evidence for further research on disease clusters and for health care providers to develop different strategies based on age and sex to improve the early identification and treatment of multimorbidity.

Clinical evidence construction of East Asian herbal medicine for inflammatory pain in rheumatoid arthritis based on integrative data mining approach

BACKGROUND

Rheumatoid arthritis (RA) is a chronic inflammatory disease that leads to a significant social burden. East Asian herbal medicine (EAHM) has long been used to treat RA. Therefore, a systematic study of how EAHM treatments can be developed into new drugs using specific materials is needed.

METHODS

Eleven databases containing literature in English, Korean, Chinese, and Japanese were searched for randomized controlled trials comparing EAHM with conventional medicine (CM). A meta-analysis was performed on the variable data to assess their effects on inflammatory pain. Subsequently, we searched for core materials and combinations of core material-based data mining methods.

RESULTS

A total of 186 trials involving 19,716 patients with RA met the inclusion criteria. According to the meta-analysis, EAHM had a significantly superior effect on continuous pain intensity, tender joint count, and response rate. Patients treated with EAHM had a significantly reduced incidence of adverse events compared with those treated with CM. Based on additional analysis of the EAHM formula data included in this meta-analysis, 21 core materials and five core herbal combinations were identified.

CONCLUSION

EAHM remedies for RA have the adequate potential for use as candidate materials for treating inflammatory pain in RA. The candidate core herbs evaluated in this study act on multiple pathways and are expected to provide pain relief, sustained inflammation suppression, immune regulation, and prevention of joint destruction. It seems worthwhile to conduct follow-up research on drug development using the core materials derived from this review.

Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines

Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols required for statistical analyses were pinpointed with a set of recommendations for the application of machine learning modeling or exploratory analyses on VAERS data with a case study of COVID-19 vaccines (Pfizer-BioNTech, Moderna, Janssen). A total of 262,454 duplicate reports (29%) from 905,976 reports were identified, which were merged into a total of 643,522 distinct reports. A customized online survey was also conducted providing 211 reports. A total of 20 highest reported adverse events were first identified. Differences in results after applying various machine learning algorithms (association rule mining, self-organizing maps, hierarchical clustering, bipartite graphs) on VAERS data were noticed. Moderna reports showed injection-site-related AEs of higher frequencies by 15.2%, consistent with the online survey (12% higher reporting rate for pain in the muscle for Moderna compared to Pfizer-BioNTech). AEs {headache, pyrexia, fatigue, chills, pain, dizziness} constituted >50% of the total reports. Chest pain in male children reports was 295% higher than in female children reports. Penicillin and sulfa were of the highest frequencies (22%, and 19%, respectively). Analysis of uncleaned VAERS data demonstrated major differences from the above (7% variations). Spelling/grammatical mistakes in allergies were discovered (e.g., ~14% reports with incorrect spellings for penicillin).

Cellphone-distracted crashes of novice teen drivers: Understanding associations of contributing factors for crash severity levels and cellphone usage types

OBJECTIVE

As novice teen drivers are uniquely susceptible to the harmful effects of secondary activities on cellphones, 38 states and Washington D.C. have banned all types of cellphone usage for drivers younger than 18 years or in the learner/intermediate phase of driving. Despite the prevalence of such cellphone prohibitions, several surveillance studies have highlighted the persistent engagement of teenagers in cellphone-distracted driving, which increases the related crash risk. Most of the prior studies broadly consider cellphone usage as a general distraction instead of investigating different distraction-related tasks associated with cellphone use. This study analyzed the cellphone crashes of novice teenagers (aged 15-17 years) to discover the grouping of contributing factors by crash severity levels and cellphone usage types.

METHODS

The current study collected five years (2015-2019) of related crash data from the Louisiana Department of Transportation and Development. A manual effort was carried out to recognize the type of cellphone tasks before collision by reading the narratives of police-investigated crash reports. Association rule mining was applied to explore the associations between numerous crash attributes in multiple circumstances without relying on any predetermined hypotheses.

RESULTS

The cumulative effect of cellphone distraction and no seatbelt usage is frequently visible in confirmed injury crash scenarios. Cellphone crashes of novice teenagers at intersections are strongly associated with talking/listening rather than texting/browsing/dialing and reaching for/answering/locating. The associations among environmental factors and modes of cellphone usage significantly influence the manner of collisions. Single-vehicle crashes are associated with cellphone manipulation while driving on weekends in cloudy weather, whereas sideswipe collisions are frequent in evening hours during reaching for/answering/locating the cellphones. In relation to texting/browsing/dialing, novice teenagers operating vans/SUVs are strongly associated with traffic control violations.

CONCLUSIONS

The findings are expected to be beneficial for policymakers and other safety officials to develop strategic planning and implementable countermeasures when dealing with cellphone-distracted novice teenagers. The association of factors identified from the analysis exhibits real-world crash scenarios critical to strengthening driver education programs to mitigate teen driver crashes. Moreover, cellphone crashes and related casualties can be reduced by eliminating or improving one of the attributes involved in the crash patterns.

Exploring offending characteristics of young people with foetal alcohol spectrum disorder in Western Australia

Neurodevelopmental impairments resulting from Foetal Alcohol Spectrum Disorder (FASD) can increase the likelihood of justice system involvement. This study compared offence characteristics in young people with FASD to demographically matched controls (n = 500) in Western Australia. A novel approach (i.e. association rule mining) was adopted to uncover relationships between personal attributes and offence characteristics. For FASD participants (n = 100), file records were reviewed retrospectively. Mean age of the total sample was 15.60 years (range = 10-24), with 82% males and 88% Australian Aboriginal. After controlling for demographic factors, regression analyses showed FASD participants were more likely than controls to be charged with reckless driving (odds ratio, OR = 4.20), breach of bail/community orders (OR = 3.19), property damage (OR = 1.84), and disorderly behaviour (OR = 1.54). Overall, our findings suggest justice-involved individuals with FASD have unique offending profiles. These results have implications for sentencing, diversionary/crime prevention programs and interventions.

Different Coping Patterns among US Graduate and Undergraduate Students during COVID-19 Pandemic: A Machine Learning Approach

COVID-19 caused unprecedented disruptions to regular university operations worldwide. Dealing with 100% virtual classrooms and suspension of essential in-person activities resulted in significant stress and anxiety for students coping with isolation, fear, and uncertainties in their academic careers. In this study, we applied a machine learning approach to identify distinct coping patterns between graduate and undergraduate students when facing these challenges. We based our study on a large proprietary dataset collected from 517 students in US professional institutions during an early peak of the pandemic. In particular, we cast our problem under the association rule mining (ARM) framework by introducing a new method to transform survey data into market basket items and customer transactions in which students' behavioral patterns were analogous to customer purchase patterns. Our experimental results suggested that graduate and undergraduate students adopted different ways of coping that could be attributed to their different maturity levels and lifestyles. Our findings can further serve as a focus of attention (FOA) tool to facilitate customized advising or counseling to address the unique challenges associated with each group that may warrant differentiated interventions.

East Asian Herbal Medicine to Reduce Primary Pain and Adverse Events in Cancer Patients : A Systematic Review and Meta-Analysis With Association Rule Mining to Identify Core Herb Combination

Objective: Cancer pain is an important factor in cancer management that affects a patient’s quality of life and survival-related outcomes. The aim of this review was to systematically evaluate the efficacy and safety of oral administration of East Asian herbal medicine (EAHM) for primary cancer pain and to explore core herb patterns based on the collected data.

Methods: A comprehensive literature search was conducted in 11 electronic databases, namely, PubMed, Cochrane Library, Cumulative Index to Nursing & Allied Health Literature, EMBASE, Korean Studies Information Service System, Research Information Service System, Oriental Medicine Advanced Searching Integrated System, Korea Citation Index, Chinese National Knowledge Infrastructure Database (CNKI), Wanfang Data, and CiNii for randomized controlled trials from their inception until August 19, 2021. Statistical analysis was performed in R version 4.1.1 and R studio program using the default settings of the meta-package. When heterogeneity in studies was detected, the cause was identified through meta-regression and subgroup analysis. Methodological quality was independently assessed using the revised tool for risk of bias in randomized trials (Rob 2.0).

Results: A total of 38 trials with 3,434 cancer pain patients met the selection criteria. Meta-analysis favored EAHM-combined conventional medicine on response rate (risk ratio: 1.06; 95% CI: 1.04 to 1.09, p < 0.0001), continuous pain intensity (standardized mean difference: −1.74; 95% CI: −2.17 to −1.30, p < 0.0001), duration of pain relief (standardized mean difference: 0.96, 95% CI: 0.69 to 1.22, p < 0.0001), performance status (weighted mean difference: 10.71; 95% CI: 4.89 to 16.53, p = 0.0003), and opioid usage (weighted mean difference: −20.66 mg/day; 95% CI: −30.22 to −11.10, p < 0.0001). No significant difference was observed between EAHM and conventional medicine on response rate and other outcomes. Patients treated with EAHM had significantly reduced adverse event (AE) incidence rates. In addition, based on the ingredients of herb data in this meta-analysis, four combinations of herb pairs, which were frequently used together for cancer pain, were derived.

Conclusion: EAHM monotherapy can decrease adverse events associated with pain management in cancer patients. Additionally, EAHM-combined conventional medicine therapy may be beneficial for patients with cancer pain in increasing the response rate, relieving pain intensity, improving pain-related performance status, and regulating opioid usage. However, the efficacy and safety of EAHM monotherapy are difficult to conclude due to the lack of methodological quality and quantity of studies. More well-designed, multicenter, double-blind, and placebo-controlled randomized clinical trials are needed in the future. In terms of the core herb combination patterns derived from the present review, four combinations of herb pairs might be promising for cancer pain because they have been often distinctly used for cancer patients in East Asia. Thus, they are considered to be worth a follow-up study to elucidate their actions and effects.

Systematic Review Registration:https://www.crd.york.ac.uk/prospero/, identifier CRD42021265804

Hypertension and Obesity: Risk Factors for Thyroid Disease

Thyroid disease instances have rapidly increased in the past few decades; however, the cause of the disease remains unclear. Understanding the pathogenesis of thyroid disease will potentially reduce morbidity and mortality rates. Currently, the identified risk factors from existing studies are controversial as they were determined through qualitative analysis and were not further confirmed by quantitative implementations. Association rule mining, as a subset of data mining techniques, is dedicated to revealing underlying correlations among multiple attributes from a complex heterogeneous dataset, making it suitable for thyroid disease pathogenesis identification. This study adopts two association rule mining algorithms (i.e., Apriori and FP-Growth Tree) to identify risk factors correlated with thyroid disease. Extensive experiments were conducted to reach impartial findings with respect to knowledge discovery through two independent digital health datasets. The findings confirmed that gender, hypertension, and obesity are positively related to thyroid disease development. The history of I₁₃₁ treatment and Triiodothyronine level can be potential factors for evaluating subsequent thyroid disease.

Another Look at Obesity Paradox in Acute Ischemic Stroke: Association Rule Mining

Though obesity is generally associated with the development of cardiovascular disease (CVD) risk factors, previous reports have also reported that obesity has a beneficial effect on CVD outcomes. We aimed to verify the existing obesity paradox through binary logistic regression (BLR) and clarify the paradox via association rule mining (ARM). Patients with acute ischemic stroke (AIS) were assessed for their 3-month functional outcome using the modified Rankin Scale (mRS) score. Predictors for poor outcome (mRS 3–6) were analyzed through BLR, and ARM was performed to find out which combination of risk factors was concurrently associated with good outcomes using maximal support, confidence, and lift values. Among 2580 patients with AIS, being obese (OR [odds ratio], 0.78; 95% CI, 0.62–0.99) had beneficial effects on the outcome at 3 months in BLR analysis. In addition, the ARM algorithm showed obese patients with good outcomes were also associated with an age less than 55 years and mild stroke severity. While BLR analysis showed a beneficial effect of obesity on stroke outcome, in ARM analysis, obese patients had a relatively good combination of risk factor profiles compared to normal BMI patients. These results may partially explain the obesity paradox phenomenon in AIS patients.

East Asian herbal medicine for cancer pain: A protocol for systematic review and meta-analysis with using association rule analysis to identify core herb pattern

BACKGROUND

Cancer pain is an important factor in cancer management that affects a patient's quality of life and survival-related outcomes. The aim of this review is to systematically evaluate the efficacy and safety of oral administration East Asian herbal medicine (EAHM) for primary cancer pain, and to explore core herb patterns based on collected data.

METHODS

A comprehensive literature search will be conducted in 10 electronic databases including PubMed, Cochrane Library, Cumulative Index to Nursing & Allied Health Literature, EMBASE, Korean Studies Information Service System, Research Information Service System Oriental Medicine Advanced Searching Integrated System, Korea Citation Index, Chinese National Knowledge Infrastructure Database (CNKI), CiNii for randomized controlled trials from their inception until August 19, 2021. Statistical analysis will be performed in the software R version 4.1.1. and R studio program using the default settings of the 'meta' package. When heterogeneity in studies is detected, the cause will be identified through meta regression and subgroup analysis. Methodological quality will be assessed independently using the revised tool for risk of bias in randomized trials (Rob 2.0).

RESULTS

This study will provide more comprehensive and specific evidence of EAHM for cancer pain management.

CONCLUSIONS

Based on the results of this review, it is expected that the efficacy and safety of East Asian herbal medicine for cancer pain may be confirmed. In addition, it will be possible to derivation of a core herb pattern related to this research topic through additional association rule mining analysis.

Predicting Anxiety in Routine Palliative Care Using Bayesian-Inspired Association Rule Mining

We propose a novel knowledge extraction method based on Bayesian-inspired association rule mining to classify anxiety in heterogeneous, routinely collected data from 9,924 palliative patients. The method extracts association rules mined using lift and local support as selection criteria. The extracted rules are used to assess the maximum evidence supporting and rejecting anxiety for each patient in the test set. We evaluated the predictive accuracy by calculating the area under the receiver operating characteristic curve (AUC). The evaluation produced an AUC of 0.89 and a set of 55 atomic rules with one item in the premise and the conclusion, respectively. The selected rules include variables like pain, nausea, and various medications. Our method outperforms the previous state of the art (AUC = 0.72). We analyzed the relevance and novelty of the mined rules. Palliative experts were asked about the correlation between variables in the data set and anxiety. By comparing expert answers with the retrieved rules, we grouped rules into expected and unexpected ones and found several rules for which experts' opinions and the data-backed rules differ, most notably with the patients' sex. The proposed method offers a novel way to predict anxiety in palliative settings using routinely collected data with an explainable and effective model based on Bayesian-inspired association rule mining. The extracted rules give further insight into potential knowledge gaps in the palliative care field.

Deciphering the Efficacy and Mechanisms of Chinese Herbal Medicine for Diabetic Kidney Disease by Integrating Web-Based Biochemical Databases and Real-World Clinical Data: Retrospective Cohort Study

Background

Diabetic kidney disease (DKD) is one of the most crucial causes of chronic kidney disease (CKD). However, the efficacy and biomedical mechanisms of Chinese herbal medicine (CHM) for DKD in clinical settings remain unclear.

Objective

This study aimed to analyze the outcomes of DKD patients with CHM-only management and the possible molecular pathways of CHM by integrating web-based biomedical databases and real-world clinical data.

Methods

A total of 152,357 patients with incident DKD from 2004 to 2012 were identified from the National Health Insurance Research Database (NHIRD) in Taiwan. The risk of mortality was estimated with the Kaplan-Meier method and Cox regression considering demographic covariates. The inverse probability of treatment weighting was used for confounding bias between CHM users and nonusers. Furthermore, to decipher the CHM used for DKD, we analyzed all CHM prescriptions using the Chinese Herbal Medicine Network (CMN), which combined association rule mining and social network analysis for all CHM prescriptions. Further, web-based biomedical databases, including STITCH, STRING, BindingDB, TCMSP, TCM@Taiwan, and DisGeNET, were integrated with the CMN and commonly used Western medicine (WM) to explore the differences in possible target proteins and molecular pathways between CHM and WM. An application programming interface was used to assess these online databases to obtain the latest biomedical information.

Results

About 13.7% (20,947/131,410) of patients were classified as CHM users among eligible DKD patients. The median follow-up duration of all patients was 2.49 years. The cumulative mortality rate in the CHM cohort was significantly lower than that in the WM cohort (28% vs 48%, P<.001). The risk of mortality was 0.41 in the CHM cohort with covariate adjustment (99% CI 0.38-0.43; P<.001). A total of 173,525 CHM prescriptions were used to construct the CMN with 11 CHM clusters. CHM covered more DKD-related proteins and pathways than WM; nevertheless, WM aimed at managing DKD more specifically. From the overrepresentation tests carried out by the online website Reactome, the molecular pathways covered by the CHM clusters in the CMN and WM seemed distinctive but complementary. Complementary effects were also found among DKD patients with concurrent WM and CHM use. The risk of mortality for CHM users under renin-angiotensin-aldosterone system (RAAS) inhibition therapy was lower than that for CHM nonusers among DKD patients with hypertension (adjusted hazard ratio [aHR] 0.47, 99% CI 0.45-0.51; P<.001), chronic heart failure (aHR 0.43, 99% CI 0.37-0.51; P<.001), and ischemic heart disease (aHR 0.46, 99% CI 0.41-0.51; P<.001).

Conclusions

CHM users among DKD patients seemed to have a lower risk of mortality, which may benefit from potentially synergistic renoprotection effects. The framework of integrating real-world clinical databases and web-based biomedical databases could help in exploring the roles of treatments for diseases.

An Association Rule Mining Analysis of Lifestyle Behavioral Risk Factors in Cancer Survivors with High Cardiovascular Disease Risk

We aimed to assess which lifestyle risk behaviors have the greatest influence on the risk of cardiovascular disease in cancer survivors and which of these behaviors are most prominently clustered in cancer survivors, using logistic regression and association rule mining (ARM). We analyzed a consecutive series of 897 cancer survivors from the Korean National Health and Nutritional Exam Survey (2012-2016). Cardiovascular disease risks were assessed using the atherosclerotic cardiovascular disease score (ASCVDs). We classified participants as being in a low-risk group if their calculated ASCVDs was less than 10% and as being in a high-risk group if their score was 10% or higher. We used association rule mining to analyze patterns of lifestyle risk behaviors by ASCVDs risk group, based upon public health recommendations described in the Alameda 7 health behaviors (current smoking, heavy drinking, physical inactivity, obesity, breakfast skipping, frequent snacking, and suboptimal sleep duration). Forty-two percent of cancer survivors had a high ASCVD. Current smoking (common odds ratio, 11.19; 95% confidence interval, 3.66-34.20, p < 0.001) and obesity (common odds ratio, 2.67; 95% confidence interval, 1.40-5.08, p < 0.001) were significant predictors of high ASCVD in cancer survivors within a multivariate model. In ARM analysis, current smoking and obesity were identified as important lifestyle risk behaviors in cancer survivors. In addition, various lifestyle risk behaviors co-occurred with smoking in male cancer survivors.

Mining Complex Genetic Patterns Conferring Multiple Sclerosis Risk

(1) Background: Complex genetic relationships, including gene-gene (G × G; epistasis), gene(n), and gene-environment (G × E) interactions, explain a substantial portion of the heritability in multiple sclerosis (MS). Machine learning and data mining methods are promising approaches for uncovering higher order genetic relationships, but their use in MS have been limited. (2) Methods: Association rule mining (ARM), a combinatorial rule-based machine learning algorithm, was applied to genetic data for non-Latinx MS cases (n = 207) and controls (n = 179). The objective was to identify patterns (rules) amongst the known MS risk variants, including HLA-DRB1*15:01 presence, HLA-A*02:01 absence, and 194 of the 200 common autosomal variants. Probabilistic measures (confidence and support) were used to mine rules. (3) Results: 114 rules met minimum requirements of 80% confidence and 5% support. The top ranking rule by confidence consisted of HLA-DRB1*15:01, SLC30A7-rs56678847 and AC093277.1-rs6880809; carriers of these variants had a significantly greater risk for MS (odds ratio = 20.2, 95% CI: 8.5, 37.5; p = 4 × 10⁻⁹). Several variants were shared across rules, the most common was INTS8-rs78727559, which was in 32.5% of rules. (4) Conclusions: In summary, we demonstrate evidence that specific combinations of MS risk variants disproportionately confer elevated risk by applying a robust analytical framework to a modestly sized study population.

Application of unsupervised machine learning to identify areas of blood product wastage in transfusion medicine

BACKGROUND

Wastage of blood products can be a significant cost to blood banks. However, the cause of wastage is often complex and makes it difficult to determine wastage-associated factors. Machine learning techniques may be useful tools to investigate these complex associations. We investigated whether unsupervised machine learning can identify patterns associated with wastage in our blood bank.

MATERIALS AND METHODS

Data on red blood cells, platelets and frozen products were obtained from the laboratory information system of the Central Zone Blood Transfusion Services at Nova Scotia Health Authority. A total of 879 532 transactions were analysed by association rule mining, a type of machine learning algorithm. Associations with lift scores greater than 25 and with clinical relevance were flagged for further examination.

RESULTS

Association rule mining returned a total of 3355 associations related to wastage. Several notable associations were identified. For example, certain wards were associated with wastage due to thawing unused frozen products. Other examples included association between smaller blood banks and evening work shifts with product wastage due to excess time outside the laboratory or returning products with high temperatures.

CONCLUSION

This paper demonstrates the effective use of unsupervised machine learning for the purpose of investigating wastage in a large blood bank. The use of association rule mining was able to identify wastage factors, which can help guide quality improvement initiatives. This technique can be automated to provide rapid analysis of complex associations contributing to wastage and could be utilized in modern blood banks.

Complementary Chinese Herbal Medicine Therapy Improves Survival in Patients With Pemphigus: A Retrospective Study From a Taiwan-Based Registry

Pemphigus is a life-threatening and skin-specific inflammatory autoimmune disease, characterized by intraepidermal blistering between the mucous membranes and skin. Chinese herbal medicine (CHM) has been used as an adjunct therapy for treating many diseases, including pemphigus. However, there are still limited studies in effects of CHM treatment in pemphigus, especially in Taiwan. To more comprehensively explore the effect of long-term CHM treatment on the overall mortality of pemphigus patients, we performed a retrospective analysis of 1,037 pemphigus patients identified from the Registry for Catastrophic Illness Patients database in Taiwan. Among them, 229 and 177 patients were defined as CHM users and non-users, respectively. CHM users were young, predominantly female, and had a lesser Charlson comorbidity index (CCI) than non-CHM users. After adjusting for age, sex, prednisolone use, and CCI, CHM users had a lower overall mortality risk than non-CHM users (multivariate model: hazard ratio (HR): 0.422, 95% confidence interval (CI): 0.242–0.735, p = 0.0023). The cumulative incidence of overall survival was significantly higher in CHM users than in non-users (p = 0.0025, log rank test). Association rule mining and network analysis showed that there was one main CHM cluster with Qi–Ju–Di–Huang–Wan (QJDHW), Dan–Shen (DanS; Radix Salviae miltiorrhizae; Salvia miltiorrhiza Bunge), Jia–Wei–Xiao–Yao-–San (JWXYS), Huang–Lian (HL; Rhizoma coptidis; Coptis chinensis Franch.), and Di–Gu–Pi (DGP; Cortex lycii; Lycium barbarum L.), while the second CHM cluster included Jin–Yin–Hua (JYH; Flos lonicerae; Lonicera hypoglauca Miq.) and Lian–Qiao (LQ; Fructus forsythiae; Forsythia suspensa (Thunb.) Vahl). In Taiwan, CHMs used as an adjunctive therapy reduced the overall mortality to approximately 20% among pemphigus patients after a follow-up of more than 6 years. A comprehensive CHM list may be useful in future clinical trials and further scientific investigations to improve the overall survival in these patients.

Understanding the Public's Emotions about Cancer: Analysis of Social Media Data

Cancer survivors suffer from emotional distress, which varies depending on several factors. However, existing emotion management programs are insufficient and do not take into consideration all of the factors. Social media provides a platform for understanding the emotions of the public. The aim of this study was to explore the relationship between the public's emotions about cancer and factors affecting emotions using social media data. We used 321,339 posts on cancer and emotions relating to cancer extracted from 22 social media channels between 1 January 2014, and 30 June 2017. The factors affecting emotions were analyzed using association rule mining and social network analysis. Hope/gratitude was the most frequently mentioned emotion group on social media followed by fear/anxiety/overwhelmed, sadness/depression/loneliness/guilt, and anger/denial. Acute survival stage, treatment method, and breast cancer were associated with hope/gratitude. Early stage, gastrointestinal problems, fatigue/pain/fever, and pancreatic cancer were associated with fear/anxiety/overwhelmed. Surgery, hair loss/skin problems, and fatigue/pain/fever were associated with sadness/depression/loneliness/guilt. Acute survival stage and hair loss/skin problems were associated with anger/denial. We found that emotions concerning cancer differed depending on the cancer type, cancer stage, survival stage, treatment, and symptoms. These findings could guide the development of tailored emotional management programs for cancer survivors that meet the public's needs more effectively.

Multivariate Top-Coding for Statistical Disclosure Limitation

One of the most challenging problems for national statistical agencies is how to release to the public microdata sets with a large number of attributes while keeping the disclosure risk of sensitive information of data subjects under control. When statistical agencies alter microdata in order to limit the disclosure risk, they need to take into account relationships between the variables to produce a good quality public data set. Hence, Statistical Disclosure Limitation (SDL) methods should not be univariate (treating each variable independently of others), but preferably multivariate, that is, handling several variables at the same time. Statistical agencies are often concerned about disclosure risk associated with the extreme values of numerical variables. Thus, such observations are often top or bottom-coded in the public use files. Top-coding consists of the substitution of extreme observations of the numerical variable by a threshold, for example, by the 99th percentile of the corresponding variable. Bottom coding is defined similarly but applies to the values in the lower tail of the distribution. We argue that a univariate form of top/bottom-coding may not offer adequate protection for some subpopulations which are different in terms of a top-coded variable from other subpopulations or the whole population. In this paper, we propose a multivariate form of top-coding based on clustering the variables into groups according to some metric of closeness between the variables and then forming the rules for the multivariate top-codes using techniques of Association Rule Mining within the clusters of variables obtained on the previous step. Bottom-coding procedures can be defined in a similar way. We illustrate our method on a genuine multivariate data set of realistic size.

Investigation of vehicle-bicycle hit-and-run crashes

OBJECTIVES

Although cycling has been promoted around the world as a sustainable mode of transportation, bicyclists are among the most vulnerable road users, subject to high injury and fatality risk. The vehicle-bicycle hit-and-run crashes degrade the morality and result in delays of medical services provided to victims. This paper aims to determine the significant factors that contribute to drivers' hit-and-run behavior in vehicle-bicycle crashes and their interdependency based on a 6-year crash dataset of Victoria, Australia, with an integrated data mining framework.

METHODS

The framework integrates imbalanced data resampling, near zero variance predictor elimination, learning-based feature extraction with random forest algorithm, and association rule mining.

RESULTS

The crash-related features that play the most important role in classifying hit-and-run crashes are identified as collision type, gender, age group, vehicle passengers involved, severity of accident, speed zone, road classification, divided road, region and peak hour.

CONCLUSIONS

The result of the paper can further provide implications on the policies and counter-measures in order to prevent bicyclists from vehicle-bicycle hit-and-run collisions.

Approaches to text mining for analyzing treatment plan of quit smoking with free-text medical records: A PRISMA-compliant meta-analysis

BACKGROUND

Smoking is a complex behavior associated with multiple factors such as personality, environment, genetics, and emotions. Text data are a rich source of information. However, pure text data requires substantial human resources and time to extract and apply the knowledge, resulting in many details not being discovered and used. This study proposes a novel approach that explores a text mining flow to capture the behavior of smokers quitting tobacco from their free-text medical records. More importantly, the paper examines the impact of these changes on smokers. The goal is to help smokers quit smoking. The study population included adult patients that were >20 years old of age who consulted the medical center's smoking cessation outpatient clinic from January to December 2016. A total of 246 patients visited the clinic in the study period. After excluding incomplete medical records or lost follow up, there were 141 patients included in the final analysis. There are 141 valid data points for patients who only treated once and patients with empty medical records. Two independent review authors will make the study selection based on the study eligibility criteria. Our participants are from all the patients that were involved in this study and the staff of Division of Family Medicine, National Taiwan University Hospital. Interventions and study appraisal are not required.

METHODS

The paper develops an algorithm for analyzing smoking cessation treatment plans documented in free-text medical records. The approach involves the development of an information extraction flow that uses a combination of data mining techniques, including text mining. It can use not only to help others quit smoking but also for other medical records with similar data elements. The Apriori associations of our algorithm from the text mining revealed several important clinical implications for physicians during smoking cessation. For example, an apparent association between nicotine replacement therapy (NRT) and other medications such as Inderal, Rivotril, Dogmatyl, and Solaxin. Inderal and Rivotril use in patients with anxiety disorders as anxiolytics frequently.

RESULTS

Finally, we find that the rules associating with NRT combination with blood tests may imply that the use of NRT combination therapy in smokers with chronic illness may result in lower abstinence. Further large-scale surveys comparing varenicline or bupropion with NRT combination in smokers with a chronic disease are warranted. The Apriori algorithm suffers from some weaknesses despite being transparent and straightforward. The main limitation is the costly wasting of time to hold a vast number of candidates sets with frequent itemsets, low minimum support, or large itemsets.

CONCLUSION

In the paper, the most visible areas for the therapeutic application of text mining are the integration and transfer of advances made in basic sciences, as well as a better understanding of the processes involved in smoking cessation. Text mining may also be useful for supporting decision-making processes associated with smoking cessation. Systematic review registration number is not registered.

Association rule mining for the ordered placement of traditional Chinese medicine containers: An experimental study

In traditional Chinese medicine (TCM) clinics, the pharmacists responsible for dispensing the herbal medicine usually find the desired ingredients based on positions of the shelves (racks; frames; stands). Generally, these containers are arranged in an alphabetical order depending on the herbal medicine they contain. However, certain related ingredients tend to be used together in many prescriptions, even though the containers may be stored far away from each other. This can cause problems, especially when there are many patients and/or the limited number of pharmacists. If the dispensing time takes longer, it is likely to impact the satisfaction of the patients' experience. Moreover, the stamina of the pharmacists will be consumed quickly.In this study, we investigate on an association rule mining technology to improve efficiency in TCM dispensing based on the frequent pattern growth algorithm and try to identify which 2 or 3 herbal medicines will match together frequently in prescriptions. Furthermore, 3 experimental studies are conducted based on a dataset collected from a traditional Chinese medicine hospital. The dataset includes information for an entire year (2014), including 4 seasons and doctors. Afterward, a questionnaire on the usefulness of the extracted rules was administered to the pharmacists in the case hospital. The responses showed the mining results to be very valuable as a reference for the placement and ordering of the frames in the TCM pharmacies and drug stores.

Why do people call crisis helplines? Identifying taxonomies of presenting reasons and discovering associations between these reasons

The objective of this study is to identify the most common reasons for contacting a crisis helpline through analysing a large call log data set. Two taxonomies were identified within the call log data from a Northern Ireland telephone crisis helpline (Lifeline), categorising the cited reason for each call. One taxonomy categorised the reasons at a fine granular level; the other taxonomy used the relatively coarser International Classification of Diseases-10. Exploratory data analytic techniques were applied to discover insights into why individuals contact crisis helplines. Risk ratings of calls were also compared to assess the associations between presenting issue and of risk of suicide as assessed. Reasons for contacting the service were assessed across geolocations. Association rule mining was used to identify associations between the presenting reasons for client's calls. Results demonstrate that both taxonomies show that calls with reasons relating to suicide are the most common reasons for contacting Lifeline and were a prominent feature of the discovered association rules. There were significant differences between reasons in both taxonomies concerning risk ratings. Reasons for calling helplines that are associated with higher risk ratings include those calling with a personality disorder, mental disorders, delusional disorders and drugs (legal). In conclusion, employing two differing taxonomy approaches to analyse call log data reveals the prevalence of main presenting reasons for contacting a crisis helpline. The association rule mining using each taxonomy provided insights into the associations between presenting reasons. Practical and research applications are discussed.

Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data

Cancer is well recognized as a complex disease with dysregulated molecular networks or modules. Graph- and rule-based analytics have been applied extensively for cancer classification as well as prognosis using large genomic and other data over the past decade. This article provides a comprehensive review of various graph- and rule-based machine learning algorithms that have been applied to numerous genomics data to determine the cancer-specific gene modules, identify gene signature-based classifiers and carry out other related objectives of potential therapeutic value. This review focuses mainly on the methodological design and features of these algorithms to facilitate the application of these graph- and rule-based analytical approaches for cancer classification and prognosis. Based on the type of data integration, we divided all the algorithms into three categories: model-based integration, pre-processing integration and post-processing integration. Each category is further divided into four sub-categories (supervised, unsupervised, semi-supervised and survival-driven learning analyses) based on learning style. Therefore, a total of 11 categories of methods are summarized with their inputs, objectives and description, advantages and potential limitations. Next, we briefly demonstrate well-known and most recently developed algorithms for each sub-category along with salient information, such as data profiles, statistical or feature selection methods and outputs. Finally, we summarize the appropriate use and efficiency of all categories of graph- and rule mining-based learning methods when input data and specific objective are given. This review aims to help readers to select and use the appropriate algorithms for cancer classification and prognosis study.

Keystone Species and Modularity in Microbial Hydrocarbon Degradation Uncovered by Network Analysis and Association Rule Mining

Natural microbial communities in soils are highly diverse, allowing for rich networks of microbial interactions to unfold. Identifying key players in these networks is difficult as the distribution of microbial diversity at the local scale is typically non-uniform, and is the outcome of both abiotic environmental factors and microbial interactions. Here, using spatially resolved microbial presence-absence data along an aquifer transect contaminated with hydrocarbons, we combined co-occurrence analysis with association rule mining to identify potential keystone species along the hydrocarbon degradation process. Derived co-occurrence networks were found to be of a modular structure, with modules being associated with specific spatial locations and metabolic activity along the contamination plume. Association rules identify species that never occur without another, hence identifying potential one-sided cross-feeding relationships. We find that hub nodes in the rule network appearing in many rules as targets qualify as potential keystone species that catalyze critical transformation steps and are able to interact with varying partners. By contrasting analysis based on data derived from bulk samples and individual soil particles, we highlight the importance of spatial sample resolution. While individual inferred interactions are hypothetical in nature, requiring experimental verification, the observed global network patterns provide a unique first glimpse at the complex interaction networks at work in the microbial world.

Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data

Background

The rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease necessitating renal replacement therapy has placed a significant strain on health care. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors, such as lifestyle choices, is vital in informing strategies toward renoprotection. Modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities, although the lifestyle risk factors and modification strategies may vary with different comorbidities (eg, diabetes, hypertension). However, there are limited studies on suitable lifestyle interventions for CKD patients with comorbidities.

Objective

The objectives of our study are to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using a US nationwide survey in combination with literature mining, and (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task, which can be generalized for similar tasks associated with noncommunicable diseases (NCDs).

Methods

We applied ARM to identify lifestyle risk factors for CKD progression with comorbidities (cardiovascular disease, chronic pulmonary disease, rheumatoid arthritis, diabetes, and cancer) using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. The BRFSS is a Web-based resource, which includes demographic information, chronic health conditions, fruit and vegetable consumption, and sugar- or salt-related behavior. To enrich the BRFSS questionnaire, the Semantic MEDLINE Database was also mined to identify lifestyle risk factors.

Results

The results suggest that lifestyle modification for CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease or rheumatoid arthritis, lifestyle modification should be high dietary fiber intake and participation in moderate-intensity exercise. Meanwhile, the management of CKD patients with diabetes focuses on exercise and weight loss predominantly.

Conclusions

We have demonstrated the use of ARM to identify lifestyle risk factors for CKD with common comorbid chronic conditions using data from BRFSS 2017. Our methods can be generalized to advance chronic disease management with more focused and optimized lifestyle modification of NCDs.

Mental disorders and medical comorbidities: Association rule mining approach

PURPOSE

This study explored the medical comorbidities of mental disorders using association rule mining.

DESIGN AND METHODS

Patients diagnosed with mental disorders between 2002 and 2010 were identified. An equal number of nonmental disorder subjects were randomly selected and matched with case group by age and gender.

FINDINGS

Sleep disorders and digestive diseases were frequent comorbidities among mental disorders. The specific medical comorbidities were diabetes mellitus, chronic liver disease, extrapyramidal diseases, disorders of stomach function, general symptoms, sleep disturbance, and family circumstances.

PRACTICE IMPLICATIONS

The results suggest that education of professional knowledge of comorbid conditions should be provided to nurses for caring patients with mental illnesses.

Shared Multidrug Resistance Patterns in Chicken-Associated Escherichia coli Identified by Association Rule Mining

Using multiple antimicrobials in food animals may incubate genetically-linked multidrug-resistance (MDR) in enteric bacteria, which can contaminate meat at slaughter. The U.S. National Antimicrobial Resistance Monitoring System tested 14,418 chicken-associated Escherichia coli between 2004 and 2012 for resistance to 15 antimicrobials, resulting in >32,000 possible MDR patterns. We analyzed MDR patterns in this dataset with association rule mining, also called market-basket analysis. The association rules were pruned with four quality measures resulting in a <1% false-discovery rate. MDR rules were more stable across consecutive years than between slaughter and retail. Rules were decomposed into networks with antimicrobials as nodes and rules as edges. A strong subnetwork of beta-lactam resistance existed in each year and the beta-lactam resistances also had strong associations with sulfisoxazole, gentamicin, streptomycin and tetracycline resistances. The association rules concur with previously identified E. coli resistance patterns but provide significant flexibility for studying MDR in large datasets.

Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining

Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further. As a kind of complex physiological state, hypertension is hard to characterize accurately. However, most existing hypertension identification methods usually extract features only from limited aspects such as the time-frequency domain or non-linear domain. It is difficult for them to characterize hypertension patterns comprehensively, which results in limited identification performance. Furthermore, existing methods can only determine whether the subjects suffer from hypertension, but they cannot give additional useful information about the patients’ condition. For example, their classification results cannot explain why the subjects are hypertensive, which is not conducive to further analyzing the patient’s condition. To this end, this paper proposes a novel hypertension identification method by integrating classification and association rule mining. Its core idea is to exploit the association relationship among multi-dimension features to distinguish hypertensive patients from normotensive subjects. In particular, the proposed method can not only identify hypertension accurately, but also generate a set of class association rules (CARs). The CARs are proved to be able to reflect the subject’s physiological status. Experimental results based on a real dataset indicate that the proposed method outperforms two state-of-the-art methods and three common classifiers, and achieves 84.4%, 82.5% and 85.3% in terms of accuracy, precision and recall, respectively.

A New Search Method Using Association Rule Mining for Drug-Drug Interaction Based on Spontaneous Report System

Background: Adverse events (AEs) can be caused not only by one drug but also by the interaction between two or more drugs. Therefore, clarifying whether an AE is due to a specific suspect drug or drug-drug interaction (DDI) is useful information for proper use of drugs. Whereas previous reports on the search for drug-induced AEs with signal detection using spontaneous reporting systems (SRSs) are numerous, reports on drug interactions are limited. This is because in methods that use “a safety signal indicator” (signal), which is frequently used in pharmacovigilance, a huge number of combinations must be prepared when signal detection is performed, and each risk index must be calculated, which makes interaction search appear unrealistic.

Objective: In this paper, we propose association rule mining (AR) using large dataset analysis as an alternative to the conventional methods (additive interaction model (AI) and multiplicative interaction model (MI)).

Methods: The data source used was the Japanese Adverse Drug Event Report database. The combination of drugs for which the risk index is detected by the “combination risk ratio (CR)” as the target was assumed to be true data, and the accuracy of signal detection using the AR methods was evaluated in terms of sensitivity, specificity, Youden's index, F-score.

Results: Our experimental results targeting Stevens-Johnson syndrome indicate that AR has a sensitivity of 99.05%, specificity of 92.60%, Youden's index of 0.917, F-score of 0.876, AI has a sensitivity of 95.62%, specificity of 96.92%, Youden's index of 0.925, and F-score of 0.924, and MI has a sensitivity of 65.46%, specificity of 98.78%, Youden's index of 0.642, and F-score of 0.771. This result was about the same level as or higher than the conventional method.

Conclusions: If you use similar calculation methods to create combinations from the database, not only for SJS, but for all AEs, the number of combinations would be so enormous that it would be difficult to perform the calculations. However, in the AR method, the “Apriori algorithm” is used to reduce the number of calculations. Thus, the proposed method has the same detection power as the conventional methods, with the significant advantage that its calculation process is simple.

ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis

For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures-weighted rank-based Jaccard and Cosine measures-and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm-RANWAR-was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.

Assessing Lightning and Wildfire Hazard by Land Properties and Cloud to Ground Lightning Data with Association Rule Mining in Alberta, Canada

Hotspot analysis was implemented to find regions in the province of Alberta (Canada) with high frequency Cloud to Ground (CG) lightning strikes clustered together. Generally, hotspot regions are located in the central, central east, and south central regions of the study region. About 94% of annual lightning occurred during warm months (June to August) and the daily lightning frequency was influenced by the diurnal heating cycle. The association rule mining technique was used to investigate frequent CG lightning patterns, which were verified by similarity measurement to check the patterns' consistency. The similarity coefficient values indicated that there were high correlations throughout the entire study period. Most wildfires (about 93%) in Alberta occurred in forests, wetland forests, and wetland shrub areas. It was also found that lightning and wildfires occur in two distinct areas: frequent wildfire regions with a high frequency of lightning, and frequent wild-fire regions with a low frequency of lightning. Further, the preference index (PI) revealed locations where the wildfires occurred more frequently than in other class regions. The wildfire hazard area was estimated with the CG lightning hazard map and specific land use types.

On Interestingness Measures for Mining Statistically Significant and Novel Clinical Associations from EMRs

Association rule mining has received significant attention from both the data mining and machine learning communities. While data mining researchers focus more on designing efficient algorithms to mine rules from large datasets, the learning community has explored applications of rule mining to classification. A major problem with rule mining algorithms is the explosion of rules even for moderate sized datasets making it very difficult for end users to identify both statistically significant and potentially novel rules that could lead to interesting new insights and hypotheses. Researchers have proposed many domain independent interestingness measures using which, one can rank the rules and potentially glean useful rules from the top ranked ones. However, these measures have not been fully explored for rule mining in clinical datasets owing to the relatively large sizes of the datasets often encountered in healthcare and also due to limited access to domain experts for review/analysis. In this paper, using an electronic medical record (EMR) dataset of diagnoses and medications from over three million patient visits to the University of Kentucky medical center and affiliated clinics, we conduct a thorough evaluation of dozens of interestingness measures proposed in data mining literature, including some new composite measures. Using cumulative relevance metrics from information retrieval, we compare these interestingness measures against human judgments obtained from a practicing psychiatrist for association rules involving the depressive disorders class as the consequent. Our results not only surface new interesting associations for depressive disorders but also indicate classes of interestingness measures that weight rule novelty and statistical strength in contrasting ways, offering new insights for end users in identifying interesting rules.

Prediction of Peaks of Seasonal Influenza in Military Health-Care Data

Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article.

Mining amino acid association patterns in class B GPCRs

Class B GPCR family is a small group of receptors which are activated by peptides of intermediate length that range from 30 to 40 amino acid residues including hormones, neuropeptides and autocrine factors that mediate diverse physiological functions. They are involved in physiological processes like glucose homeostasis (glucagon and glucagon-like peptide-1), calcium homeostasis and bone turnover (parathyroid hormone and calcitonin), and control of the stress axis (corticotropin-releasing factor). Most of the GPCR structures and their functions are still unknown. Thus, the study of amino acid association patterns can be useful in prediction of their structure and functions. In view of above, in this paper, an attempt has been made to explore amino acid association patterns in class B GPCRs and their relationships with secondary structures and physiochemical properties. The fuzzy association rule mining is employed to take care of uncertainty due to variation in length of sequences. The association rules have been generated with the help of patterns discovered in the sequences.

Disease Comorbidity Network Guides the Detection of Molecular Evidence for the Link Between Colorectal Cancer and Obesity

Epidemiological studies suggested that obesity increases the risk of colorectal cancer (CRC). The genetic connection between CRC and obesity is multifactorial and inconclusive. In this study, we hypothesize that the study of shared comorbid diseases between CRC and obesity can offer unique insights into common genetic basis of these two diseases. We constructed a comorbidity network based on mining health data for millions of patients. We developed a novel approach and extracted the diseases that play critical roles in connecting obesity and CRC in the comorbidity network. Our approach was able to prioritize metabolic syndrome and diabetes, which are known to be associated with obesity and CRC through insulin resistance pathways. Interestingly, we found that osteoporosis was highly associated with the connection between obesity and CRC. Through gene expression meta-analysis, we identified novel genes shared among CRC, obesity and osteoporosis. Literature evidences support that these genes may contribute in explaining the genetic overlaps between obesity and CRC.

A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions

The computational or in silico approaches for analysing the HIV-1-human protein-protein interaction (PPI) network, predicting different host cellular factors and PPIs and discovering several pathways are gaining popularity in the field of HIV research. Although there exist quite a few studies in this regard, no previous effort has been made to review these works in a comprehensive manner. Here we review the computational approaches that are devoted to the analysis and prediction of HIV-1-human PPIs. We have broadly categorized these studies into two fields: computational analysis of HIV-1-human PPI network and prediction of novel PPIs. We have also presented a comparative assessment of these studies and proposed some methodologies for discussing the implication of their results. We have also reviewed different computational techniques for predicting HIV-1-human PPIs and provided a comparative study of their applicability. We believe that our effort will provide helpful insights to the HIV research community.

PHARM - Association Rule Mining for Predictive Health

Predictive health is a new and innovative healthcare model that focuses on maintaining health rather than treating diseases. Such a model may benefit from computer-based decision support systems, which provide more quantitative health assessment, enabling more objective advice and action plans from predictive health providers. However, data mining for predictive health is more challenging compared to that for diseases. This is a reason why there are relatively fewer predictive health decision support systems embedded with data mining. The purpose of this study is to research and develop an interactive decision support system, called PHARM, in conjunction with Emory Center for Health Discovery and Well Being (CHDWB®). PHARM adopts association rule mining to generate quantitative and objective rules for health assessment and prediction. A case study results in 12 rules that predict mental illness based on five psychological factors. This study shows the value and usability of the decision support system to prevent the development of potential illness and to prioritize advice and action plans for reducing disease risks.