Medical big data: promise and challenges

Item response theory as a feature selection and interpretation tool in the context of machine learning

Optimizing the number and utility of features to use in a classification analysis has been the subject of many research studies. Most current models use end-classifications as part of the feature reduction process, leading to circularity in the methodology. The approach demonstrated in the present research uses item response theory (IRT) to select features independent of the end-classification results without the biased accuracies that this circularity engenders. Dichotomous and polytomous IRT models were used to analyze 30 histological breast cancer features from 569 patients using the Wisconsin Diagnostic Breast Cancer data set. Based on their characteristics, three features were selected for use in a machine learning classifier. For comparison purposes, two machine learning-based feature selection protocols were run-recursive feature elimination (RFE) and ridge regression-and the three features selected from these analyses were also used in the subsequent learning classifier. Classification results demonstrated that all three selection processes performed comparably. The non-biased nature of the IRT protocol and information provided about the specific characteristics of the features as to why they are of use in classification help to shed light on understanding which attributes of features make them suitable for use in a machine learning context.

Big Data and Digital Solutions: Laying the Foundation for Cardiovascular Population Management CME

There are huge gaps in evidence-based cardiovascular care at the national, organizational, practice, and provider level that can be attributed to variation in provider attitudes, lack of incentives for positive change and care standardization, and observed uncertainty in clinical decision making. Big data analytics and digital application platforms-such as patient care dashboards, clinical decision support systems, mobile patient engagement applications, and key performance indicators-offer unique opportunities for value-based healthcare delivery and efficient cardiovascular population management. Successful implementation of big data solutions must include a multidisciplinary approach, including investment in big data platforms, harnessing technology to create novel digital applications, developing digital solutions that can inform the actions of clinical and policy decision makers and relevant stakeholders, and optimizing engagement strategies with the public and information-empowered patients.

Matchmaker, matchmaker make me a match: Opportunities and challenges in optimizing compatibility of HLA eplets in transplantation

The development of donor-specific antibodies (DSAs) is a major complication in transplantation, which is associated with inferior graft survival, impaired quality of life, and increased healthcare costs. DSA develop upon recognition of nonself HLA by the recipient's immune system. HLA molecules contain epitopes, which are the surface regions of HLA molecules recognized by antibodies. HLAMatchmaker is an algorithm for assessing donor:recipient HLA compatibility at the level of structurally defined HLA targets called eplets. The consideration of eplets, rather than the whole HLA molecule, could offer some advantages when classifying the immune risk associated with particular donor:recipient pairs. Assessing compatibility at the level of HLA eplets could decrease misclassification of post-transplant immune risk by improving specificity, when antibodies are confirmed to be directed against donor eplets missing from the recipient's repertoire of eplets. Consideration of eplets may also increase the sensitivity of immune risk assessment, when identifying mismatched eplets that could give rise to new, not previously detected, donor-specific antibodies post-transplant. Eplet matching can serve as a rational strategy for immune risk mitigation. Herein, we review the evolution of HLA (in) compatibility assessment for organ allocation. We outline challenges in the implementation of eplet-based donor:recipient matching, including unavailability of allele-level donor genotypes for 11 HLA loci at the time of organ allocation and difficulty in assessing the hierarchy of immune risk associated with particular HLA eplet mismatches. Opportunities to address some of the current shortcomings of donor genotyping and HLAMatchmaker are also discussed. While there is a demonstrated benefit in the application of HLAMatchmaker for donor: recipient HLA (in)compatibility assessment, evolving long-read genotyping methods, compilation of large data sets with allele-level genotypes, and standardization of methods to verify eplets as determinants of immune-mediated injuries are required before HLA eplet matching is implemented in organ allocation to improve upon transplant outcomes.

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.

Deep neural network models for computational histopathology: A survey

Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to disease progression and patient survival outcomes. Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting histology images. In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis. From the survey of over 130 papers, we review the field's progress based on the methodological aspect of different machine learning strategies such as supervised, weakly supervised, unsupervised, transfer learning and various other sub-variants of these methods. We also provide an overview of deep learning based survival models that are applicable for disease-specific prognosis tasks. Finally, we summarize several existing open datasets and highlight critical challenges and limitations with current deep learning approaches, along with possible avenues for future research.

Editorial Commentary: Big Databases Are Not All Created Equal: Interpret Their Studies With Caution

"Big Data" studies are extremely important in orthopedic research, as randomized controlled trials with extremely large sample sizes are nearly impossible to perform. But, as always, the devil is in the details. Databases are only as good as the information that is put into them. And when evaluating these studies, let's not forget how to interpret them. Many factors-patient insurance status, age, socioeconomic status, ethnicity, and comorbidities; regional variations; hospital status (inpatient/outpatient); clerical errors, recording biases, and omission of relevant orthopedic outcome measures; temporal changes in billing codes; payer mix; population demographics and catchment area; and data collection methodology-mean that the results of a specific big data study may or may not be generalizable to other populations.

Using propensity scores to estimate effects of treatment initiation decisions: State of the science

Confounding can cause substantial bias in nonexperimental studies that aim to estimate causal effects. Propensity score methods allow researchers to reduce bias from measured confounding by summarizing the distributions of many measured confounders in a single score based on the probability of receiving treatment. This score can then be used to mitigate imbalances in the distributions of these measured confounders between those who received the treatment of interest and those in the comparator population, resulting in less biased treatment effect estimates. This methodology was formalized by Rosenbaum and Rubin in 1983 and, since then, has been used increasingly often across a wide variety of scientific disciplines. In this review article, we provide an overview of propensity scores in the context of real-world evidence generation with a focus on their use in the setting of single treatment decisions, that is, choosing between two therapeutic options. We describe five aspects of propensity score analysis: alignment with the potential outcomes framework, implications for study design, estimation procedures, implementation options, and reporting. We add context to these concepts by highlighting how the types of comparator used, the implementation method, and balance assessment techniques have changed over time. Finally, we discuss evolving applications of propensity scores.

Identifying Patients With Axial Spondyloarthritis in Large Datasets: Expanding Possibilities for Observational Research

OBJECTIVE

Observational research of axial spondyloarthritis (axSpA) is limited by a lack of methods for identifying diverse axSpA phenotypes in large datasets. Algorithms were previously designed to identify a broad spectrum of patients with axSpA, including patients not identifiable with diagnosis codes. The study objective was to estimate the performance of axSpA identification methods in the general Veterans Affairs (VA) population.

METHODS

A patient sample with known axSpA status (n = 300) was established with chart review. For feasibility, this sample was enriched with veterans with axSpA risk factors. Algorithm performance outcomes included sensitivities, positive predictive values (PPV), and F1 scores (an overall performance metric combining sensitivity and PPV). Performance was estimated with unweighted outcomes for the axSpA-enriched sample and inverse probability weighted (IPW) outcomes for the general VA population. These outcomes were also assessed for traditional identification methods using diagnosis codes for the ankylosing spondylitis (AS) subtype of axSpA.

RESULTS

The mean age was 54.7 and 92% were male. Unweighted F1 scores (0.59-0.74) were higher than IPW F1 scores (0.48-0.65). The full algorithm had the best overall performance (F1_IPW 0.65). The Early Algorithm was the most inclusive (sensitivity_IPW 0.90, PPV_IPW 0.38). The traditional method using ≥ 2 AS diagnosis codes from rheumatology had the highest PPV (PPV_IPW 0.84, sensitivity_IPW 0.34).

CONCLUSION

The axSpA identification methods demonstrated a range of performance attributes in the general VA population that may be appropriate for various types of studies. The novel identification algorithms may expand the scope of research by enabling identification of more diverse axSpA populations.

How to Determine the Early Warning Threshold Value of Meteorological Factors on Influenza through Big Data Analysis and Machine Learning

Infectious diseases are a major health challenge for the worldwide population. Since their rapid spread can cause great distress to the real world, in addition to taking appropriate measures to curb the spread of infectious diseases in the event of an outbreak, proper prediction and early warning before the outbreak of the threat of infectious diseases can provide an important basis for early and reasonable response by the government health sector, reduce morbidity and mortality, and greatly reduce national losses. However, if only traditional medical data is involved, it may be too late or too difficult to implement prediction and early warning of an infectious outbreak. Recently, medical big data has become a research hotspot and has played an increasingly important role in public health, precision medicine, and disease prediction. In this paper, we focus on exploring a prediction and early warning method for influenza with the help of medical big data. It is well known that meteorological conditions have an influence on influenza outbreaks. So, we try to find a way to determine the early warning threshold value of influenza outbreaks through big data analysis concerning meteorological factors. Results show that, based on analysis of meteorological conditions combined with influenza outbreak history data, the early warning threshold of influenza outbreaks could be established with reasonable high accuracy.

Exploring Fever of Unknown Origin Intelligent Diagnosis Based on Clinical Data: Model Development and Validation

Background

Fever of unknown origin (FUO) is a group of diseases with heterogeneous complex causes that are misdiagnosed or have delayed diagnoses. Previous studies have focused mainly on the statistical analysis and research of the cases. The treatments are very different for the different categories of FUO. Therefore, how to intelligently diagnose FUO into one category is worth studying.

Objective

We aimed to fuse all of the medical data together to automatically predict the categories of the causes of FUO among patients using a machine learning method, which could help doctors diagnose FUO more accurately.

Methods

In this paper, we innovatively and manually built the FUO intelligent diagnosis (FID) model to help clinicians predict the category of the cause and improve the manual diagnostic precision. First, we classified FUO cases into four categories (infections, immune diseases, tumors, and others) according to the large numbers of different causes and treatment methods. Then, we cleaned the basic information data and clinical laboratory results and structured the electronic medical record (EMR) data using the bidirectional encoder representations from transformers (BERT) model. Next, we extracted the features based on the structured sample data and trained the FID model using LightGBM.

Results

Experiments were based on data from 2299 desensitized cases from Peking Union Medical College Hospital. From the extensive experiments, the precision of the FID model was 81.68% for top 1 classification diagnosis and 96.17% for top 2 classification diagnosis, which were superior to the precision of the comparative method.

Conclusions

The FID model showed excellent performance in FUO diagnosis and thus would be a potentially useful tool for clinicians to enhance the precision of FUO diagnosis and reduce the rate of misdiagnosis.

Maximizing the reusability of gene expression data by predicting missing metadata

Reusability is part of the FAIR data principle, which aims to make data Findable, Accessible, Interoperable, and Reusable. One of the current efforts to increase the reusability of public genomics data has been to focus on the inclusion of quality metadata associated with the data. When necessary metadata are missing, most researchers will consider the data useless. In this study, we developed a framework to predict the missing metadata of gene expression datasets to maximize their reusability. We found that when using predicted data to conduct other analyses, it is not optimal to use all the predicted data. Instead, one should only use the subset of data, which can be predicted accurately. We proposed a new metric called Proportion of Cases Accurately Predicted (PCAP), which is optimized in our specifically-designed machine learning pipeline. The new approach performed better than pipelines using commonly used metrics such as F1-score in terms of maximizing the reusability of data with missing values. We also found that different variables might need to be predicted using different machine learning methods and/or different data processing protocols. Using differential gene expression analysis as an example, we showed that when missing variables are accurately predicted, the corresponding gene expression data can be reliably used in downstream analyses.

Classification, Categorization, and Algorithms for Articular Cartilage Defects

There is a critical unmet need in the clinical implementation of valid preventative and therapeutic strategies for patients with articular cartilage pathology based on the significant gap in understanding of the relationships between diagnostic data, disease progression, patient-related variables, and symptoms. In this article, the current state of classification and categorization for articular cartilage pathology is discussed with particular focus on machine learning methods and the authors propose a bedside-bench-bedside approach with highly quantitative techniques as a solution to these hurdles. Leveraging computational learning with available data toward articular cartilage pathology patient phenotyping holds promise for clinical research and will likely be an important tool to identify translational solutions into evidence-based clinical applications to benefit patients. Recommendations for successful implementation of these approaches include using standardized definitions of articular cartilage, to include characterization of depth, size, location, and number; using measurements that minimize subjectivity or validated patient-reported outcome measures; considering not just the articular cartilage pathology but the whole joint, and the patient perception and perspective. Application of this approach through a multistep process by a multidisciplinary team of clinicians and scientists holds promise for validating disease mechanism-based phenotypes toward clinically relevant understanding of articular cartilage pathology for evidence-based application to orthopaedic practice.

Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach

BACKGROUND

Inpatient falls, many resulting in injury or death, are a serious problem in hospital settings. Existing falls risk assessment tools, such as the Morse Fall Scale, give a risk score based on a set of factors, but don't necessarily signal which factors are most important for predicting falls. Artificial intelligence (AI) methods provide an opportunity to improve predictive performance while also identifying the most important risk factors associated with hospital-acquired falls. We can glean insight into these risk factors by applying classification tree, bagging, random forest, and adaptive boosting methods applied to Electronic Health Record (EHR) data.

OBJECTIVE

The purpose of this study was to use tree-based machine learning methods to determine the most important predictors of inpatient falls, while also validating each via cross-validation.

MATERIALS AND METHODS

A case-control study was designed using EHR and electronic administrative data collected between January 1, 2013 to October 31, 2013 in 14 medical surgical units. The data contained 38 predictor variables which comprised of patient characteristics, admission information, assessment information, clinical data, and organizational characteristics. Classification tree, bagging, random forest, and adaptive boosting methods were used to identify the most important factors of inpatient fall-risk through variable importance measures. Sensitivity, specificity, and area under the ROC curve were computed via ten-fold cross validation and compared via pairwise t-tests. These methods were also compared to a univariate logistic regression of the Morse Fall Scale total score.

RESULTS

In terms of AUROC, bagging (0.89), random forest (0.90), and boosting (0.89) all outperformed the Morse Fall Scale (0.86) and the classification tree (0.85), but no differences were measured between bagging, random forest, and adaptive boosting, at a p-value of 0.05. History of Falls, Age, Morse Fall Scale total score, quality of gait, unit type, mental status, and number of high fall risk increasing drugs (FRIDs) were considered the most important features for predicting inpatient fall risk.

CONCLUSIONS

Machine learning methods have the potential to identify the most relevant and novel factors for the detection of hospitalized patients at risk of falling, which would improve the quality of patient care, and to more fully support healthcare provider and organizational leadership decision-making. Nurses would be able to enhance their judgement to caring for patients at risk for falls. Our study may also serve as a reference for the development of AI-based prediction models of other iatrogenic conditions. To our knowledge, this is the first study to report the importance of patient, clinical, and organizational features based on the use of AI approaches.

Using Medical Big Data to Develop Personalized Medicine for Dry Eye Disease

Dry eye disease (DED) is a chronic, multifactorial ocular surface disorder with multiple etiologies that results in tear film instability. Globally, the prevalence of DED is expected to increase with an aging society and daily use of digital devices. Unfortunately, the medical field is currently unprepared to meet the medical needs of patients with DED. Noninvasive, reliable, and readily reproducible biomarkers have not yet been identified, and the current mainstay treatment for DED relies on symptom alleviation using eye drops with no effective preventative therapies available. Medical big data analyses, mining information from multiomics studies and mobile health applications, may offer a solution for managing chronic conditions such as DED. Omics-based data on individual physiologic status may be leveraged to prevent high-risk diseases, accurately diagnose illness, and improve patient prognosis. Mobile health applications enable the portable collection of real-world medical data and biosignals through personal devices. Together, these data lay a robust foundation for personalized treatments for various ocular surface diseases and other pathologies that currently lack the components of precision medicine. To fully implement personalized and precision medicine, traditional aggregate medical data should not be applied directly to individuals without adjustments for personal etiology, phenotype, presentation, and symptoms.

New medical big data for P4 medicine on allergic conjunctivitis

Allergic conjunctivitis affects approximately 15-20% of the global population and can permanently deteriorate one's quality of life (QoL) and work productivity, leading to societal work force costs. Although not fully understood, allergic conjunctivitis is a multifactorial disease with a complex network of environmental, lifestyle, and host contributory risk factors. To effectively enhance the quality of treatment for patients with allergic conjunctivitis, as well as other allergic diseases, the field must first comprehend the pathology underlying various individualized subjective symptoms and stratify the disease according to risk factors and presentations. Such competent stratification and societal reconstruction that targets the alleviation of the damage due to allergic diseases would greatly help ramify personalized treatments and prevent the projected increase in societal costs imposed by allergic diseases. Owing to the rapid advancements in the information and technology sector, medical big data are greatly accessible and useful to decipher the pathophysiology of many diseases. Such data collected through multi-omics and mobile health have been effective for research on chronic diseases including allergic and immune-mediated diseases. Novel big data containing vast and continuous information on individuals with allergic conjunctivitis and other allergic symptoms are being used to search for causative genes of diseases, gain insights into new biomarkers, prevent disease progression, and, ultimately, improve QoL. The individualized and holistic data accrued from new angles using technological innovations are helping the field realize the principles of P4 medicine: predictive, preventive, personalized, and participatory medicine.

Big Data Applications in Medical Field: A Literature Review

Digital imaging and medical reporting have acquired an essential role in healthcare, but the main challenge is the storage of a high volume of patient data. Although newer technologies are already introduced in the medical sciences to save records size, Big Data provides advancements by storing a large amount of data to improve the efficiency and quality of patient treatment with better care. It provides intelligent automation capabilities to reduce errors than manual inputs. Large numbers of research papers on big data in the medical field are studied and analyzed for their impacts, benefits, and applications. Big data has great potential to support the digitalization of all medical and clinical records and then save the entire data regarding the medical history of an individual or a group. This paper discusses big data usage for various industries and sectors. Finally, 12 significant applications for the medical field by the implementation of big data are identified and studied with a brief description. This technology can be gainfully used to extract useful information from the available data by analyzing and managing them through a combination of hardware and software. With technological advancement, big data provides health-related information for millions of patient-related to life issues such as lab tests reporting, clinical narratives, demographics, prescription, medical diagnosis, and related documentation. Thus, Big Data is essential in developing a better yet efficient analysis and storage healthcare services. The demand for big data applications is increasing due to its capability of handling and analyzing massive data. Not only in the future but even now, Big Data is proving itself as an axiom of storing, developing, analyzing, and providing overall health information to the physicians.

Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework

Healthcare is considered as one of the most promising application areas for artificial intelligence and analytics (AIA) just after the emergence of the latter. AI combined to analytics technologies is increasingly changing medical practice and healthcare in an impressive way using efficient algorithms from various branches of information technology (IT). Indeed, numerous works are published every year in several universities and innovation centers worldwide, but there are concerns about progress in their effective success. There are growing examples of AIA being implemented in healthcare with promising results. This review paper summarizes the past 5 years of healthcare applications of AIA, across different techniques and medical specialties, and discusses the current issues and challenges, related to this revolutionary technology. A total of 24,782 articles were identified. The aim of this paper is to provide the research community with the necessary background to push this field even further and propose a framework that will help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for chronic care patients, who present the most complex comorbidities and care needs.

Big Data Solutions for Controversies in Breast Cancer Treatment

The digital world of data is expanding with an annual growth rate of 40%, and health care is among the fastest growing sector of the digital world with an annual growth rate of 48%. Rapid growth in technology has augmented data generation; for example, electronic health records produce huge amounts of patient-level data, whereas national registries capture information on numerous factors affecting health care delivery and patient outcomes. This big data can be utilized to improve health care outcomes. This review discusses relevant applications in breast cancer treatment.

Validity of the Work Assessment Triage Tool for Selecting Rehabilitation Interventions for Workers' Compensation Claimants with Musculoskeletal Conditions

Purpose The Work Assessment Triage Tool (WATT) is a clinical decision support tool developed using machine learning to help select interventions for patients with musculoskeletal disorders. The WATT categorizes patients based on individual characteristics according to likelihood of successful return to work following rehabilitation. A previous validation showed acceptable classification accuracy, but we re-examined accuracy using a new dataset drawn from the same system 2 years later. Methods A population-based cohort design was used, with data extracted from a Canadian compensation database on workers considered for rehabilitation between January 2013 and December 2016. Data were obtained on demographic, clinical, and occupational characteristics, type of rehabilitation undertaken, and return to work outcomes. Analysis included classification accuracy statistics of WATT recommendations. Results The sample included 28,919 workers (mean age 43.9 years, median duration 56 days), of whom 23,124 experienced a positive outcome within 30 days following return to work assessment. Sensitivity of the WATT for selecting successful programs was 0.13 while specificity was 0.87. Overall accuracy was 0.60 while human recommendations were higher at 0.72. Conclusions Overall accuracy of the WATT for selecting successful rehabilitation programs declined in a more recent cohort and proved less accurate than human clinical recommendations. Algorithm revision and further validation is needed.

Revaccination management of a large cohort of pediatric patients following a potential lapse in cold storage

INTRODUCTION

In a pediatric clinic in California (US), 3823 patients were vaccinated with potentially-compromised vaccines following lapses in cold storage chain management between February 2014 and April 2015. A revaccination program was initiated in May 2015. Families were contacted by mail and encouraged to discuss follow-up options with their care team, namely: revaccination, serological testing and/or revaccination, or no further action. This study aimed: to understand which families were more likely to respond to the outreach, and to engage in any testing and/or revaccination; to determine whether or not vaccination with these potentially-compromised vaccines elicited sufficient immune response in pediatric patients; and to estimate the program cost.

METHODS

Patients who had received potentially-compromised vaccines were identified, and relevant data were extracted from their electronic health records. Logistic regression analyses were performed to identify factors associated with response to outreach, serological testing and/or revaccination.

RESULTS

3823 patients between 0 and 21 years received an average of 3.1 potentially-compromised vaccines. 2547 revaccinations were performed (1515 patients) and 544 patients had serological testing results. Non-immune titer levels were only reported for 3-4% and 8% of the tested patients who had received potentially-compromised tetanus and hepatitis B vaccines, respectively, and only for children two years old and younger. Three years after the revaccination program started, 77% of all cases were considered resolved and 62.5% of patients (1970/3152) who were administered potentially-compromised vaccines were either revaccinated or had seroprotective titers. Response to outreach and decision to choose serological testing and/or revaccinate were affected by patient age, race/ethnicity and zip code median income (p < 0.05).

CONCLUSION

We observed race/ethnicity, patient age and income differences in response to the outreach and decision-making. For patients vaccinated with potentially-compromised vaccines, serological testing should be considered prior to revaccination. Revaccination may not be the most appropriate course of action for all patients.

Hospital Disparities between Native Hawaiian and Other Pacific Islanders and Non-Hispanic Whites with Alzheimer's Disease and Related Dementias

Objective: To compare important indicators of quality of care between Native Hawaiians and other Pacific Islanders (NHOPIs) and non-Hispanic Whites (NHWs) with Alzheimer's disease and related dementias (ADRD). Methods: We used the Health Care Cost and Utilization Project, Hawaii State Inpatient Databases, 2010-2014. They included 10,645 inpatient encounters from 7,145 NHOPI or NHW patients age ≥ 50 years, residing in Hawaii, and with at least one ADRD diagnosis in the discharge record. Outcome variables were inpatient mortality, length of hospital stay, and hospital readmission. Results: NHOPIs with ADRD had, on average, a hospital stay of .94 days less than NHWs with ADRD but were 1.16 times more likely than NHWs to be readmitted. Discussion: These patterns have important clinical care implications for NHOPIs and NHWs with ADRD as they are important indicators of quality of care. Future studies should consider specific contributors to these differences in order to develop appropriate interventions.

Correlates of physical activity behavior in adults: a data mining approach

PURPOSE

A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior.

METHODS

Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors.

RESULTS

Of the 4582 participants with valid accelerometer data at the latest follow-up, 2701 and 1881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B = 26.5, LPA: B = - 16.1, and MVPA: B = - 11.7), normalized heart rate recovery 60 s after exercise (SED: B = -16.1, LPA: B = 9.9, and MVPA: B = 9.6), average weekday total sitting time (SED: B = 34.1, LPA: B = -25.3, and MVPA: B = -5.8), and extravagance score (SED: B = 6.3 and LPA: B = - 3.7).

CONCLUSIONS

Using data mining, we established a data-driven model composed of 36 different factors of relative importance from empirical data. This model may be used to identify subgroups for multilevel intervention allocation and design. Additionally, this study methodologically discovered an extensive set of factors that can be a basis for additional hypothesis testing in PA correlates research.

Big Data Research in Neuro-Ophthalmology: Promises and Pitfalls

BACKGROUND

Big data clinical research involves application of large data sets to the study of disease. It is of interest to neuro-ophthalmologists but also may be a challenge because of the relative rarity of many of the diseases treated.

EVIDENCE ACQUISITION

Evidence for this review was gathered from the authors' experiences performing analysis of large data sets and review of the literature.

RESULTS

Big data sets are heterogeneous, and include prospective surveys, medical administrative and claims data and registries compiled from medical records. High-quality studies must pay careful attention to aspects of data set selection, including potential bias, and data management issues, such as missing data, variable definition, and statistical modeling to generate appropriate conclusions. There are many studies of neuro-ophthalmic diseases that use big data approaches.

CONCLUSIONS

Big data clinical research studies complement other research methodologies to advance our understanding of human disease. A rigorous and careful approach to data set selection, data management, data analysis, and data interpretation characterizes high-quality studies.

Predicting breast cancer risk using interacting genetic and demographic factors and machine learning

Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a combination of interacting genetic variants (SNPs) and demographic risk factors for BC, especially factors related to both familial history (Group 1) and oestrogen metabolism (Group 2), for predicting BC risk. This approach identifies the best combinations of interacting genetic and demographic risk factors that yield the highest BC risk prediction accuracy. In tests on the Kuopio Breast Cancer Project (KBCP) dataset, our approach achieves a mean average precision (mAP) of 77.78 in predicting BC risk by using interacting genetic and Group 1 features, which is better than the mAPs of 74.19 and 73.65 achieved using only Group 1 features and interacting SNPs, respectively. Similarly, using interacting genetic and Group 2 features yields a mAP of 78.00, which outperforms the system based on only Group 2 features, which has a mAP of 72.57. Furthermore, the gene interaction maps built from genes associated with SNPs that interact with demographic risk factors indicate important BC-related biological entities, such as angiogenesis, apoptosis and oestrogen-related networks. The results also show that demographic risk factors are individually more important than genetic variants in predicting BC risk.

Short-term Revision Risk of Patellofemoral Arthroplasty Is High: An Analysis from Eight Large Arthroplasty Registries

BACKGROUND

Patellofemoral arthroplasty (PFA) is one option for the treatment of isolated patellofemoral osteoarthritis, but there are limited data regarding the procedure and results. Because isolated patellofemoral arthritis is relatively uncommon, available case series generally are small, and even within national registries, sample sizes are limited. Combining data from multiple registries may aid in assessing worldwide PFA usage and survivorship.

QUESTIONS/PURPOSES

We combined and compared data from multiple large arthroplasty registries worldwide to ask: (1) What proportion of patients undergoing primary knee arthroplasty have PFA? (2) What are the patient and prosthesis characteristics associated with PFA in common practice, as reflected in registries? (3) What is the survivorship free from revision of PFA and what are the reasons for and types of revisions?

METHODS

Data were provided by eight registries that are members of the International Society of Arthroplasty Registries (ISAR) who agreed to share aggregate data: Australia, New Zealand, Canada, Sweden, Finland, Norway, the Netherlands, and the United States. De-identified data were obtained for PFA performed from either the beginning of year 2000, or the earliest recorded implantation date after that in each individual registry when PFA data collection commenced, up to December 31, 2016. This included patient demographics, implant use, all-cause revision rate (determined by cumulative percent revision [CPR]), and reasons for and type of revision.

RESULTS

During the data collection period, 6784 PFAs were performed in the eight countries. PFAs comprised less than 1% of primary knee replacements in all registries. Patient demographics were comparable in all countries. Patients were generally more likely to be women than men, and the mean age ranged from 50 years to 60 years. All registries showed a high rate of revision for PFA. The 5-year CPR for any reason ranged from 8.0% (95% CI 4.5 to 11.5) in Norway to 18.1% (95% CI 15.5 to 20.7) in the Netherlands. The most common reason for revision across all countries was disease progression (42%, 434 of 1034). Most PFAs (83%, 810 of 980) were revised to a TKA.

CONCLUSIONS

The revision risk of PFA in all registries surveyed was more than three times higher than the reported revision risk of TKA at the same times. The survivorship of PFA is similar to that of the no-longer-used procedure of metal-on-metal conventional hip replacement. Although there may be potential functional benefits from PFA, these findings of consistent and alarmingly high rates of revision should create concern, particularly as this procedure is often used in younger patients.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Ten-year patient journey of stage III non-small cell lung cancer patients: A single-center, observational, retrospective study in Korea (Realtime autOmatically updated data warehOuse in healTh care; UNIVERSE-ROOT study)

INTRODUCTION

Until the recent approval of immunotherapy after completing concurrent chemoradiotherapy (CCRT), there has been little progress in treating unresectable stage III non-small cell lung cancer (NSCLC). This prompted us to search real-world data (RWD) to better understand diagnosis and treatment patterns, and outcomes.

METHODS

This non-interventional observational study used a unique, novel algorithm for big data analysis to collect and assess anonymized patient electronic medical records from a clinical data warehouse (CDW) over a 10-year period to capture real-world patterns of diagnosis, treatment, and outcomes of stage III NSCLC patients. We describe real-world patterns of diagnosis and treatment of patients with newly-diagnosed stage III NSCLC, and patients' characteristics, and assessment of treatment outcomes.

RESULTS

We analyzed clinical variables from 23,735 NSCLC patients. Stage III patients (N = 4138, 18.2 %) were diagnosed as IIIA (N = 2,547, 11.2 %) or IIIB (N = 1,591. 7.0 %). Treated stage III patients (N = 2530, 61.1 %) had a median age of 64.2 years, were mostly male (78.5 %) and had an ECOG performance status of 1 (65.2 %). Treatment comprised curative-intent surgery (N = 1,254, 49.6 %) with 705 receiving neoadjuvant therapy; definitive CRT (N = 648, 25.6 %); palliative CT (N = 270, 10.7 %), or thoracic RT (N = 170, 6.7 %). Median OS (range) for neoadjuvant, surgery, CRT, palliative chemotherapy, lung RT alone, and supportive care was 49.2 (42.0-56.5), 52.5 (43.1-61.9), 30.3 (26.6-34.0), 14.7 (13.0-16.4), 8.8 (6.2-11.3), and 2.0 (1.0-3.0) months, respectively.

CONCLUSIONS

This unique in-house algorithm enabled a rapid and comprehensive analysis of big data through a CDW, with daily automatic updates that documented real-world PFS and OS consistent with the published literature, and real-world treatment patterns and clinical outcomes in stage III NSCLC patients.

Nationwide validation study of diagnostic algorithms for inflammatory bowel disease in Korean National Health Insurance Service database

BACKGROUND AND AIM

We conducted a nationwide validation study of diagnostic algorithms to identify cases of inflammatory bowel disease (IBD) within the Korea National Health Insurance System (NHIS) database.

METHOD

Using the NHIS dataset, we developed 44 algorithms combining the International Classification of Diseases (ICD)-10 codes, codes for Rare and Intractable Diseases (RID) registration and claims data for health care encounters, and pharmaceutical prescriptions for IBD-specific drugs. For each algorithm, we compared the case identification results from electronic medical records data with the gold standard (chart-based diagnosis). A multiple sampling test verified the validation results from the entire study population.

RESULTS

A random nationwide sample of 1697 patients (848 potential cases and 849 negative control cases) from 17 hospitals were included for validation. A combination of the ICD-10 code, ≥ 1 claims for health care encounters, and ≥ 1 prescription claims (reference algorithm) achieved excellent performance (sensitivity, 93.1% [95% confidence interval 91-94.7]; specificity, 98.1% [96.9-98.8]; positive predictive value, 97.5% [96.1-98.5]; negative predictive value, 94.5% [92.8-95.8]) with the lowest error rate (4.2% [3.3-5.3]). The multiple sampling test confirmed that the reference algorithm achieves the best performance regarding IBD diagnosis. Algorithms including the RID registration codes exhibited poorer performance compared with that of the reference algorithm, particularly for the diagnosis of patients affiliated with secondary hospitals. The performance of the reference algorithm showed no statistical difference depending on the hospital volume or IBD type, with P-value < 0.05.

CONCLUSIONS

We strongly recommend the reference algorithm as a uniform standard operational definition for future studies using the NHIS database.

A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer's disease therapy: Analysis of the blarcamesine (ANAVEX2-73) Phase 2a clinical study

INTRODUCTION

The search for drugs to treat Alzheimer's disease (AD) has failed to yield effective therapies. Here we report the first genome-wide search for biomarkers associated with therapeutic response in AD. Blarcamesine (ANAVEX2-73), a selective sigma-1 receptor (SIGMAR1) agonist, was studied in a 57-week Phase 2a trial (NCT02244541). The study was extended for a further 208 weeks (NCT02756858) after meeting its primary safety endpoint.

METHODS

Safety, clinical features, pharmacokinetic, and efficacy, measured by changes in the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Cooperative Study-Activities of Daily Living scale (ADCS-ADL), were recorded. Whole exome and transcriptome sequences were obtained for 21 patients. The relationship between all available patient data and efficacy outcome measures was analyzed with unsupervised formal concept analysis (FCA), integrated in the Knowledge Extraction and Management (KEM) environment.

RESULTS

Biomarkers with a significant impact on clinical outcomes were identified at week 57: mean plasma concentration of blarcamesine (slope MMSE:P < .041), genomic variants SIGMAR1 p.Gln2Pro (ΔMMSE:P < .039; ΔADCS-ADL:P < .063) and COMT p.Leu146fs (ΔMMSE:P < .039; ΔADCS-ADL:P < .063), and baseline MMSE score (slope MMSE:P < .015). Their combined impact on drug response was confirmed at week 148 with linear mixed effect models.

DISCUSSION

Confirmatory Phase 2b/3 clinical studies of these patient selection markers are ongoing. This FCA/KEM analysis is a template for the identification of patient selection markers in early therapeutic development for neurologic disorders.

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as "big data", has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.

A pilot study on the epidemiology of hyperuricemia in Chinese adult population based on big data from Electronic Medical Records 2014 to 2018

BACKGROUND

We performed this study based on big data from Electronic Medical Records (EMR) of outpatients and inpatients from 52 hospitals in China to investigate the prevalence of hyperuricemia in Chinese adults.

METHODS

In this retrospective, descriptive study, a total of 3,363,016 subjects from 52 hospitals in 13 provinces and municipalities in China were enrolled. Eligible subjects were 18 years and older performing serum uric acid test between 2014 and 2018. Subjects were divided into the total group (including the subjects from all the clinic departments) and department-amended group (including the subjects from all the departments except endocrinology, orthopedics, and rheumatology and immunology departments).

RESULTS

The prevalence of hyperuricemia in the department-amended group was lower than that in the total group (23.06% and 23.42% in 2018, respectively; P<0.0001). From 2014 to 2017, the prevalence of hyperuricemia increased year by year (18.29%, 20.02%, 20.16% and 23.06%, respectively) in the department-amended group. Besides, the prevalence of hyperuricemia was higher in men than that in women (38.00% and 11.89%, respectively; P<0.0001) and higher in southern region than in northern region (25.84% and 9.79%, respectively; P<0.0001) in department-amended group in 2018.

CONCLUSIONS

Projections from our study estimate that about 271 million Chinese adults aged 18 years and older may have had hyperuricemia in 2018. These findings will be useful for the future researches and healthcare decision.

Big Data and Digitalization in Dentistry: A Systematic Review of the Ethical Issues

Big Data and Internet and Communication Technologies (ICT) are being increasingly implemented in the healthcare sector. Similarly, research in the field of dental medicine is exploring the potential beneficial uses of digital data both for dental practice and in research. As digitalization is raising numerous novel and unpredictable ethical challenges in the biomedical context, our purpose in this study is to map the debate on the currently discussed ethical issues in digital dentistry through a systematic review of the literature. Four databases (Web of Science, Pub Med, Scopus, and Cinahl) were systematically searched. The study results highlight how most of the issues discussed by the retrieved literature are in line with the ethical challenges that digital technologies are introducing in healthcare such as privacy, anonymity, security, and informed consent. In addition, image forgery aimed at scientific misconduct and insurance fraud was frequently reported, together with issues of online professionalism and commercial interests sought through digital means.

Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer's disease

Alzheimer's disease (AD)-a complex disease showing multiple pathomechanistic alterations-is triggered by nonlinear dynamic interactions of genetic/epigenetic and environmental risk factors, which, ultimately, converge into a biologically heterogeneous disease. To tackle the burden of AD during early preclinical stages, accessible blood-based biomarkers are currently being developed. Specifically, next-generation clinical trials are expected to integrate positive and negative predictive blood-based biomarkers into study designs to evaluate, at the individual level, target druggability and potential drug resistance mechanisms. In this scenario, systems biology holds promise to accelerate validation and qualification for clinical trial contexts of use-including proof-of-mechanism, patient selection, assessment of treatment efficacy and safety rates, and prognostic evaluation. Albeit in their infancy, systems biology-based approaches are poised to identify relevant AD "signatures" through multifactorial and interindividual variability, allowing us to decipher disease pathophysiology and etiology. Hopefully, innovative biomarker-drug codevelopment strategies will be the road ahead towards effective disease-modifying drugs.
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Clinical anisotropy: A case for shared decision making in the age of too much data and patient dis-integration

Today, in the age of big data, we are more capable than ever before. But even having the world at our disposal with naught but the touch of a button, we find ourselves exceedingly vulnerable in the patient chair. With insurmountable amounts of knowledge being published and disseminated around the world, how can clinicians keep up and what can be done about it? And sitting in the patient chair, bewildered by the ever-changing landscape of medicine at the blink of an eye, how can we, as patients, ever hope to be part of the conversations revolving around our own health? In this work, we explore the present-day problems of big data in the clinical context, how failing to integrate patients can result in detrimental outcomes, and what shared decision making can do about it.

Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

Medication-induced acute kidney injury (AKI) is a well-known problem in clinical medicine. This paper reports the first development of a visual analytics (VA) system that examines how different medications associate with AKI. In this paper, we introduce and describe VISA_M3R3, a VA system designed to assist healthcare researchers in identifying medications and medication combinations that associate with a higher risk of AKI using electronic medical records (EMRs). By integrating multiple regression models, frequent itemset mining, data visualization, and human-data interaction mechanisms, VISA_M3R3 allows users to explore complex relationships between medications and AKI in such a way that would be difficult or sometimes even impossible without the help of a VA system. Through an analysis of 595 medications using VISA_M3R3, we have identified 55 AKI-inducing medications, 24,212 frequent medication groups, and 78 medication groups that are associated with AKI. The purpose of this paper is to demonstrate the usefulness of VISA_M3R3 in the investigation of medication-induced AKI in particular and other clinical problems in general. Furthermore, this research highlights what needs to be considered in the future when designing VA systems that are intended to support gaining novel and deep insights into massive existing EMRs.

Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets

In highly non-linear datasets, attributes or features do not allow readily finding visual patterns for identifying common underlying behaviors. Therefore, it is not possible to achieve classification or regression using linear or mildly non-linear hyperspace partition functions. Hence, supervised learning models based on the application of most existing algorithms are limited, and their performance metrics are low. Linear transformations of variables, such as principal components analysis, cannot avoid the problem, and even models based on artificial neural networks and deep learning are unable to improve the metrics. Sometimes, even when features allow classification or regression in reported cases, performance metrics of supervised learning algorithms remain unsatisfyingly low. This problem is recurrent in many areas of study as, per example, the clinical, biotechnological, and protein engineering areas, where many of the attributes are correlated in an unknown and very non-linear fashion or are categorical and difficult to relate to a target response variable. In such areas, being able to create predictive models would dramatically impact the quality of their outcomes, generating an immediate added value for both the scientific and general public. In this manuscript, we present RV-Clustering, a library of unsupervised learning algorithms, and a new methodology designed to find optimum partitions within highly non-linear datasets that allow deconvoluting variables and notoriously improving performance metrics in supervised learning classification or regression models. The partitions obtained are statistically cross-validated, ensuring correct representativity and no over-fitting. We have successfully tested RV-Clustering in several highly non-linear datasets with different origins. The approach herein proposed has generated classification and regression models with high-performance metrics, which further supports its ability to generate predictive models for highly non-linear datasets. Advantageously, the method does not require significant human input, which guarantees a higher usability in the biological, biomedical, and protein engineering community with no specific knowledge in the machine learning area.

Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned

OBJECTIVES

Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information.

DATA SOURCES

We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data," with special focus on outcomes prediction and major clinical concerns in critical care.

STUDY SELECTION

Three reviewers independently screened preliminary citation lists.

DATA EXTRACTION

Summary data were tabulated for review.

DATA SYNTHESIS

To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices.

CONCLUSIONS

Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care.

Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions

OBJECTIVE

Development of systematic approaches for understanding and assessing data quality is becoming increasingly important as the volume and utilization of health data steadily increases. In this study, a taxonomy of data defects was developed and utilized when automatically detecting defects to assess Medicaid data quality maintained by one of the states in the United States.

MATERIALS AND METHODS

There were more than 2.23 million rows and 32 million cells in the Medicaid data examined. The taxonomy was developed through document review, descriptive data analysis, and literature review. A software program was created to automatically detect defects by using a set of constraints whose development was facilitated by the taxonomy.

RESULTS

Five major categories and seventeen subcategories of defects were identified. The major categories are missingness, incorrectness, syntax violation, semantic violation, and duplicity. More than 3 million defects were detected indicating substantial problems with data quality. Defect density exceeded 10% in five tables. The majority of the data defects belonged to format mismatch, invalid code, dependency-contract violation, and implausible value types. Such contextual knowledge can support prioritized quality improvement initiatives for the Medicaid data studied.

CONCLUSIONS

This research took the initial steps to understand the types of data defects and detect defects in large healthcare datasets. The results generally suggest that healthcare organizations can potentially benefit from focusing on data quality improvement. For those purposes, the taxonomy developed and the approach followed in this study can be adopted.

Using big data to promote precision oral health in the context of a learning healthcare system

There has been a call for evidence-based oral healthcare guidelines, to improve precision dentistry and oral healthcare delivery. The main challenges to this goal are the current lack of up-to-date evidence, the limited integrative analytical data sets, and the slow translations to routine care delivery. Overcoming these issues requires knowledge discovery pipelines based on big data and health analytics, intelligent integrative informatics approaches, and learning health systems. This article examines how this can be accomplished by utilizing big data. These data can be gathered from four major streams: patients, clinical data, biological data, and normative data sets. All these must then be uniformly combined for analysis and modelling and the meaningful findings can be implemented clinically. By executing data capture cycles and integrating the subsequent findings, practitioners are able to improve public oral health and care delivery.

The applications of machine learning in plastic and reconstructive surgery: protocol of a systematic review

BACKGROUND

Machine learning, a subset of artificial intelligence, is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information and use it to perform various kinds of decision-making under uncertain conditions. This can assist surgeons in clinical decision-making by identifying patient cohorts that will benefit from surgery prior to treatment. The aim of this review is to evaluate the applications of machine learning in plastic and reconstructive surgery.

METHODS

A literature review will be undertaken of EMBASE, MEDLINE and CENTRAL (1990 up to September 2019) to identify studies relevant for the review. Studies in which machine learning has been employed in the clinical setting of plastic surgery will be included. Primary outcomes will be the evaluation of the accuracy of machine learning models in predicting a clinical diagnosis and post-surgical outcomes. Secondary outcomes will include a cost analysis of those models. This protocol has been prepared using the Preferred Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines.

DISCUSSION

This will be the first systematic review in available literature that summarises the published work on the applications of machine learning in plastic surgery. Our findings will provide the basis of future research in developing artificial intelligence interventions in the specialty.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42019140924.

An overview of drug discovery efforts for eczema: why is this itch so difficult to scratch?

Introduction: Atopic dermatitis (AD) is a type of allergic/inflammatory dermatitis characterized by itch and an impairment in quality of life.Areas covered: Herein, the authors review drug discovery efforts for AD, highlighting the clinical efficacy of novel drugs, with a particular focus on the relief of pruritus. Topical agents include emollients, topical antihistamines, corticosteroids, calcineurin inhibitors and herbs. Recently, topical phosphodiesterase E4 (PDE4) inhibitors like crisaborole have become available and are efficacious for mild to moderate AD with few side effects. For more severe AD, monoclonal antibodies like dupilumab are considered as efficacious subcutaneous treatment options. In severe and recalcitrant AD, systemic treatment can ameliorate AD symptoms.Expert opinion: Many topical and systemic medications have demonstrated therapeutic benefits for AD. Indeed, randomized trials have shown that topical PDE4 inhibitors and subcutaneous dupilumab are safe and efficacious. Objective tools to evaluate itch and gauge treatment efficacy is important, but current methodology relies primarily on clinical scores. AD is a systemic atopic disease with a lot of complicated psychosocial issues. Suboptimal efficacy is often due to poor compliance and unrealistic expectation of curative treatment, rendering treatment difficult despite the existence of effective medications.

Fluency and rule breaking behaviour in the frontal cortex

Design (DF) and phonemic fluency tests (FAS; D-KEFS, 2001) are commonly used to investigate voluntary generation. Despite this, several important issues remain poorly investigated. In a sizeable sample of patients with focal left or right frontal lesion we established that voluntary generation performance cannot be accounted for by fluid intelligence. For DF we found patients performed significantly worse than healthy controls (HC) only on the switch condition. However, no significant difference between left and right frontal patients was found. In contrast, left frontal patients were significantly impaired when compared with HC and right frontal patients on FAS. These lateralization findings were complemented, for the first time, by three neuroimaging; investigations. A traditional frontal subgrouping method found significant differences on FAS between patients with or without Left Inferior Frontal Gyrus lesions involving BA 44 and/or 45. Parcel Based Lesion Symptom Mapping (PLSM) found lower scores on FAS were significantly associated with damage to posterior Left Middle Frontal Gyrus. An increase in rule break errors, so far only anecdotally reported, was associated with damage to the left dorsal anterior cingulate and left body of the corpus callosum, supporting the idea that conflict resolution and monitoring impairments may play a role. Tractwise statistical analysis (TSA) revealed that patients with disconnection; in the left anterior thalamic projections, frontal aslant tract, frontal; orbitopolar tract, pons, superior longitudinal fasciculus I and II performed significantly worse than patients without disconnection in these tracts on FAS. In contrast, PLSM and TSA analyses did not reveal any significant relationship between lesion location and performance on the DF switch condition. Overall, these findings suggest DF may have limited utility as a tool in detecting lateralized frontal executive dysfunction, whereas FAS and rule break behavior appears to be linked to a set of well localized left frontal grey matter regions and white matter tracts.

Brief introduction of medical database and data mining technology in big data era

Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.

Trends and Future Direction of the Clinical Decision Support System in Traditional Korean Medicine

Objectives

The Clinical Decision Support System (CDSS), which analyzes and uses electronic health records (EHR) for medical care, pursues patient-centered medical care. It is necessary to establish the CDSS in Korean medical services for objectification and standardization. For this purpose, analyses were performed on the points to be followed for CDSS implementation with a focus on herbal medicine prescription.

Methods

To establish the CDSS in the prescription of Traditional Korean Medicine, the current prescription practices of Traditional Korean Medicine doctors were analyzed. We also analyzed whether the prescription support function of the electronic chart was implemented. A questionnaire survey was conducted querying Traditional Korean Medicine doctors working at Traditional Korean Medicine clinics and hospitals, to investigate their desired CDSS functions, and their perceived effects on herbal medicine prescription. The implementation of the CDSS among the audit software developers used by the Korean medical doctors was examined.

Results

On average, 41.2% of Traditional Korean Medicine doctors working in Traditional Korean Medicine clinics manipulated 1 to 4 herbs, and 31.2% adjusted 4 to 7 herbs. On average, 52.5% of Traditional Korean Medicine doctors working in Traditional Korean Medicine hospitals adjusted 1 to 4 herbs, and 35.5% adjusted 4 to 7 herbs. Questioning the desired prescription support function in the electronic medical record system, the Traditional Korean Medicine doctors working at Korean medicine clinics desired information on ‘medicine name, meridian entry, flavor of medicinals, nature of medicinals, efficacy,’ ‘herb combination information’ and ‘search engine by efficacy of prescription.’ The doctors also desired compounding contraindications (eighteen antagonisms, nineteen incompatibilities) and other contraindicatory prescriptions, ‘medicine information’ and ‘prescription analysis information through basic constitution analyses.’ The implementation of prescription support function varied by clinics and hospitals.

Conclusion

In order to implement and utilize the CDSS in a medical service, clinical information must be generated and managed in a standardized form. For this purpose, standardization of terminology, coding of prescriptions using a combination of herbal medicines, and unification such as the preparation method and the weights and measures should be integrated.

Symptomatology differences of major depression in psychiatric versus general hospitals: A machine learning approach

BACKGROUND

Symptomatology differences of major depressive disorder (MDD) in psychiatric and general hospitals in China leads to possible misdiagnosis. Looking at the symptomatology of first-visit patients with MDD in different mental health services, and identifying predictors of health-seeking behavior using machine learning may help to improve diagnostic accuracy.

METHODS

1500 patients first diagnosed with MDD were recruited from 16 psychiatric hospitals and 16 general hospitals across China. Socio-demographic characteristics, causal attribution, symptoms of depression within and outside Diagnostic and Statistical Manual of Mental Disorders (DSM) framework were collected using a self-made questionnaire. A predictive model of 62 variables was established using Random forest, symptom frequencies of patients in general hospitals and psychiatric hospitals were compared.

RESULTS

The machine learning approach revealed that symptoms were strong predictors of health-seeking behavior among patients with MDD. General hospitals patients had higher frequencies of suicidal ideation (χ²=15.230, p<0.001), psychosis (χ²=14.264, p<0.001), weight change (all p<0.001), hypersomnia (χ²=25.940, p<0.001), and a tendency of denying emotional/cognitive symptoms compared with psychiatric hospitals patients.

LIMITATIONS

Stigma and preference bias were not measured. Severity of current depressive episodes was not assessed. Data of previous episode(s) was not presented.

CONCLUSIONS

Symptom evaluation targeting specific patient population in different hospitals is crucial for diagnostic accuracy. Suicide prevention reliant on collaboration between general hospitals and psychiatric hospitals is required in the future construction of Chinese mental health system.

EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases

BACKGROUND

Tremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs).

METHODS

A multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated.

RESULTS

Three overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice.

CONCLUSION

These EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.

SICE: an improved missing data imputation technique

In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these data is a major concern to the stakeholders, efficiently handling missing values becomes more important. In this paper, we have proposed a new technique for missing data imputation, which is a hybrid approach of single and multiple imputation techniques. We have proposed an extension of popular Multivariate Imputation by Chained Equation (MICE) algorithm in two variations to impute categorical and numeric data. We have also implemented twelve existing algorithms to impute binary, ordinal, and numeric missing values. We have collected sixty-five thousand real health records from different hospitals and diagnostic centers of Bangladesh, maintaining the privacy of data. We have also collected three public datasets from the UCI Machine Learning Repository, ETH Zurich, and Kaggle. We have compared the performance of our proposed algorithms with existing algorithms using these datasets. Experimental results show that our proposed algorithm achieves 20% higher F-measure for binary data imputation and 11% less error for numeric data imputations than its competitors with similar execution time.