Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

The global incidence of infectious diseases has increased in recent years, posing a significant threat to human health. Hospitals typically serve as frontline institutions for detecting infectious diseases. However, accurately identifying warning signals of infectious diseases in a timely manner, especially emerging infectious diseases, can be challenging. Consequently, there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals. This paper examines the role of medical data in the early identification of infectious diseases, explores early warning technologies for infectious disease recognition, and assesses monitoring and early warning mechanisms for infectious diseases. We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems, in compliance with national strategies to integrate clinical treatment and disease prevention. Furthermore, hospitals should establish institution-specific, clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

Outlier detection using iterative adaptive mini-minimum spanning tree generation with applications on medical data

As an important technique for data pre-processing, outlier detection plays a crucial role in various real applications and has gained substantial attention, especially in medical fields. Despite the importance of outlier detection, many existing methods are vulnerable to the distribution of outliers and require prior knowledge, such as the outlier proportion. To address this problem to some extent, this article proposes an adaptive mini-minimum spanning tree-based outlier detection (MMOD) method, which utilizes a novel distance measure by scaling the Euclidean distance. For datasets containing different densities and taking on different shapes, our method can identify outliers without prior knowledge of outlier percentages. The results on both real-world medical data corpora and intuitive synthetic datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art methods.

Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image

Lung cancer is the cancer with the highest mortality rate and the highest incidence in the world at this stage. Among them, non-small lung cancer is the most common type of lung cancer, and most small cancers have disappeared, which is the optimal time for surgery at the time of diagnosis. To explore and systematically evaluate the clinical efficacy of CYP1B1 gene polymorphism in the treatment of epidermal growth factor receptor (EGFR) Mutant non-small cell lung cancer, this article proposes the principles of lung cancer screening based on CYP1B1 gene polymorphism and polarization imaging and explores the diagnosis and treatment of non-EGFR mutant lung cancer. Based on a large number of medical image data, imageomics can directly reflect the correlation between tumor molecular phenotype and image characteristics by deeply mining some imaging features of the image, which has important value in the early diagnosis of disease, the formulation of personalized treatment plan, and efficacy evaluation and prognosis prediction. A total of 141 NSCLC patients with sensitive EGFR mutation were included in this study, including 101 patients with EGFR single-gene mutation and 40 patients with EGFR multigene mutation coexisting mutation. Both groups of patients were female, aged ≥60 years, no smoking history, no family history of leukemia, adenocarcinoma, lung cancer, stage IV, lymph node metastasis, living, far from metastasis, and ECOG score of 0-2. This study examined the relative number of gene expression and PFS in EGFR multigene co-existing mutations. When the number of mixed genes is 1, 2, and higher, the PFS is 9 months, 8 months, and 6 months, respectively. The PFS time of this group of patients gradually shortened. Therefore, this study examined the benefit of polygenic mutation in estimation by comparing the clinical characteristics of patients with EGFR single-gene mutation and polygenic mutation, to provide measurement of EGFR-TKI and to provide suggestions for future drug selection.

Method for Assessing the Influence of Phobic Stimuli in Virtual Simulators

In the organizing of professional training, the assessment of the trainee's reaction and state in stressful situations is of great importance. Phobic reactions are a specific type of stress reaction that, however, is rarely taken into account when developing virtual simulators, and are a risk factor in the workplace. A method for evaluating the impact of various phobic stimuli on the quality of training is considered, which takes into account the time, accuracy, and speed of performing professional tasks, as well as the characteristics of electroencephalograms (the amplitude, power, coherence, Hurst exponent, and degree of interhemispheric asymmetry). To evaluate the impact of phobias during experimental research, participants in the experimental group performed exercises in different environments: under normal conditions and under the influence of acrophobic and arachnophobic stimuli. The participants were divided into subgroups using clustering algorithms and an expert neurologist. After that, a comparison of the subgroup metrics was carried out. The research conducted makes it possible to partially confirm our hypotheses about the negative impact of phobic effects on some participants in the experimental group. The relationship between the reaction to a phobia and the characteristics of brain activity was revealed, and the characteristics of the electroencephalogram signal were considered as the metrics for detecting a phobic reaction.

Sharing Data With Shared Benefits: Artificial Intelligence Perspective

Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical and health applications, data need to be collected and brought together over multiple centers. However, due to various reasons, including data privacy, not all data can be made publicly available or shared with other parties. Federated and swarm learning can help in these scenarios. However, in the private sector, such as between companies, the incentive is limited, as the resulting AI models would be available for all partners irrespective of their individual contribution, including the amount of data provided by each party. Here, we explore a potential solution to this challenge as a viewpoint, aiming to establish a fairer approach that encourages companies to engage in collaborative data analysis and AI modeling. Within the proposed approach, each individual participant could gain a model commensurate with their respective data contribution, ultimately leading to better diagnostic tools for all participants in a fair manner.

Dibenzo[b,f]oxepine Molecules Used in Biological Systems and Medicine

In this short review, including 113 references, issues related to dibenzo[b,f]oxepine derivatives are presented. Dibenzo[b,f]oxepine scaffold is an important framework in medicinal chemistry, and its derivatives occur in several medicinally relevant plants. At the same time, the structure, production, and therapeutic effects of dibenzo[b,f]oxepines have not been extensively discussed thus far and are presented in this review. This manuscript addresses the following issues: extracting dibenzo[b,f]oxepines from plants and its significance in medicine, the biosynthesis of dibenzo[b,f]oxepines, the active synthetic dibenzo[b,f]oxepine derivatives, the potential of dibenzo[b,f]oxepines as microtubule inhibitors, and perspective for applications of dibenzo[b,f]oxepine derivatives. In conclusion, this review describes studies on various structural features and pharmacological actions of dibenzo[b,f]oxepine derivatives.

Modeling Patient Treatment Trajectories Using Markov Chains for Cost Analysis

Electronically stored medical records offer a rich source of data for investigating treatment trajectories and identifying best practices in healthcare. These trajectories, which consist of medical interventions, give us a foundation to evaluate the economics of treatment patterns and model the treatment paths. The aim of this work is to introduce a technical solution for the aforementioned tasks. The developed tools use the open source Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership Common Data Model to construct treatment trajectories and implement these to compose Markov models for composing financial analysis between standard of care and alternatives.

Comparative analysis of weka-based classification algorithms on medical diagnosis datasets

BACKGROUND

With the advent of 5G and the era of Big Data, the rapid development of medical information technology around the world, the massive application of electronic medical records and cases, and the digitization of medical equipment and instruments, a large amount of data has accumulated in the database system of hospitals, which includes clinical diagnosis data and hospital management data.

OBJECTIVE

This study aimed to examine the classification effects of different machine learning algorithms on medical datasets so as to better explore the value of machine learning methods in aiding medical diagnosis.

METHODS

The classification datasets of four different medical fields in the University of California Irvine machine learning database were used as the research object. Also, six categories of classification models based on the Bayesian theorem idea, integrated learning idea, and rule-based and tree-based idea were constructed using the Weka platform.

RESULTS

The between-group experiments showed that the Random Forest algorithm achieved the best results on the Indian liver disease patient dataset (ILPD), delivery cardiotocography (CADG), and lymphatic tractography (LYMP) datasets, followed by Bagging and partition and regression tree. In the within-group algorithm comparison experiments, the Bagging algorithm achieved better results than other algorithms based on the integration idea for 11 metrics on all datasets, mainly focusing on 2 binary datasets. Logit Boost had only 7 metrics with significant performance, and the best algorithm was Rotation Forest, with 28 metrics achieving optimal values. Among the algorithms based on tree ideas, the logistic model tree algorithm achieved optimal results on all metrics on the mammographic dataset (MAGR). The classification performance of BFTree, J48, and Random Tree was poor on each dataset. The best algorithm was Random Forest on the ILPD, CADG, and LYMP datasets with 27 metrics reaching the optimum.

CONCLUSION

Machine learning algorithms have good application value in disease prediction and can provide a reference basis for disease diagnosis.

Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review

The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.

Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach

Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.

Flexible cloglog links for binomial regression models as an alternative for imbalanced medical data

The complementary log-log link was originally introduced in 1922 to R. A. Fisher, long before the logit and probit links. While the last two links are symmetric, the complementary log-log link is an asymmetrical link without a parameter associated with it. Several asymmetrical links with an extra parameter were proposed in the literature over last few years to deal with imbalanced data in binomial regression (when one of the classes is much smaller than the other); however, these do not necessarily have the cloglog link as a special case, with the exception of the link based on the generalized extreme value distribution. In this paper, we introduce flexible cloglog links for modeling binomial regression models that include an extra parameter associated with the link that explains some unbalancing for binomial outcomes. For all cases, the cloglog is a special case or the reciprocal version loglog link is obtained. A Bayesian Markov chain Monte Carlo inference approach is developed. Simulations study to evaluate the performance of the proposed algorithm is conducted and prior sensitivity analysis for the extra parameter shows that a uniform prior is the most convenient for all models. Additionally, two applications in medical data (age at menarche and pulmonary infection) illustrate the advantages of the proposed models.

Pre-training in Medical Data: A Survey

Medical data refers to health-related information associated with regular patient care or as part of a clinical trial program. There are many categories of such data, such as clinical imaging data, bio-signal data, electronic health records (EHR), and multi-modality medical data. With the development of deep neural networks in the last decade, the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods’ performance in a data-limited scenario. In recent years, studies of pre-training in the medical domain have achieved significant progress. To summarize these technology advancements, this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data. In this survey, we summarize a large number of related publications and the existing benchmarking in the medical domain. Especially, the survey briefly describes how some pre-training methods are applied to or developed for medical data. From a data-driven perspective, we examine the extensive use of pre-training in many medical scenarios. Moreover, based on the summary of recent pre-training studies, we identify several challenges in this field to provide insights for future studies.

Expression of miR-320 and miR-204 in myocardial infarction and correlation with prognosis and degree of heart failure

Myocardial infarction is a very dangerous cardiovascular disease with a high mortality rate under the modern developed medical technology. miRNA is a small molecule regulatory RNA discovered in recent years, which can play an important role in many cancers and other diseases. Medical data, machine learning and medical care strategies supporting the Internet of Things (IoMT) have certain applications in the treatment of myocardial infarction. However, the specific pathogenesis of myocardial infarction is still unclear. Therefore, this paper aimed to explore the expression of microRNA-320 and microRNA-204 in myocardial infarction and used the expression of microRNA-320 and microRNA-204 to predict the prognosis of patients with myocardial infarction. In order to discuss the expression of microRNA-320 and microRNA-204 in myocardial infarction in more detail. In this paper, 40 patients in the trial period were selected for clinical research, and 10 patients with normal cardiac function were selected in NHF group as control group. 10 patients with heart failure were selected as AMHF group. 10 patients with acute myocardial infarction were selected as AMNHF group. 10 patients with heart failure after old myocardial infarction were selected as OMHF group. AMHF group, AMNHF group and OMHF group were taken as the case group. This paper analyzed the difference of miR between different groups and determined that there were significant differences in the expression of miR-320 and miR-204 between different groups. Finally, the expression and prognosis of miR-320 and miR-204 in myocardial infarction were analyzed. The analysis results showed that the expression of microRNA-320 and microRNA-204 can inhibit the activity of myocardial cells. On the fifth day, the corresponding expression of microRNA-320 and microRNA-204 reduced the optical density of myocardial cells to 1.75 and 1.76, which was significantly lower than that on the first day. Moreover, excessive miR-320 expression and excessive miR-204 expression can increase the apoptosis rate of myocardial cells. The above results indicated that the high expression of microRNA-320 and microRNA-204 can be a bad prognostic factor in patients with myocardial infarction, showing that medical data, machine learning and medical care strategies supporting IoMT can play a role in the treatment of myocardial infarction. Therefore, it is urgent to understand the pathogenesis of heart failure after myocardial infarction and find new treatment schemes to improve the positive prognosis.

Crowdsourcing smartphone data for biomedical research: Ethical and legal questions

The use of smartphones has greatly increased in the last decade and has revolutionized the way that health data are being collected and shared. Mobile applications leverage the ubiquity and technological sophistication of modern smartphones to record and process a variety of metrics relevant to human health, including behavioral measures, clinical data, and disease symptoms. Information processed by mobile applications may have significant utility for increasing biomedical knowledge, both through conventional research and emerging discovery paradigms such as citizen science. However, the ways in which smartphone-collected data may be used in nontraditional modes of biomedical discovery are not well understood, such as using data to train artificially intelligent algorithms and for product development purposes. This paper argues that the use of mobile health data for algorithm training and product development is (a) likely to become a prominent fixture in medicine, (b) likely to raise significant ethical and legal challenges, and (c) warrants immediate scrutiny by policymakers and scholars. We introduce the concept of "smartphone-crowdsourced medical data," or SCMD, and set out a broad research agenda for addressing concerns associated with this new and potentially momentous practice. We conclude that SCMD for algorithm training raises a number of ethical and legal issues which require further scholarly attention to ensure that individual interests are protected and that emerging health information sources can be used in ways that maximally, and safely, promote medical innovation.

A New Way of Investigating the Relationship Between Fasting Blood Sugar Level and Drinking Glucose Solution

The existing t-test of a correlation coefficient works under a determinate environment. In uncertainty, the existing t-test of a correlation coefficient is unable to investigate the significance of correlation. The study presents a modification of the existing t-test of a correlation coefficient using neutrosophic statistics. The test statistic is designed to investigate the significance of correlation when imprecise observations or uncertainties in the level of significance are presented. The test is applied to data obtained from patients with diabetes. From the data analysis, the proposed t-test of a correlation coefficient is found to be more effective than existing tests.

Machine Learning Assisted Cervical Cancer Detection

Cervical malignant growth is the fourth most typical reason for disease demise in women around the globe. Cervical cancer growth is related to human papillomavirus (HPV) contamination. Early screening made cervical cancer a preventable disease that results in minimizing the global burden of cervical cancer. In developing countries, women do not approach sufficient screening programs because of the costly procedures to undergo examination regularly, scarce awareness, and lack of access to the medical center. In this manner, the expectation of the individual patient's risk becomes very high. There are many risk factors relevant to malignant cervical formation. This paper proposes an approach named CervDetect that uses machine learning algorithms to evaluate the risk elements of malignant cervical formation. CervDetect uses Pearson correlation between input variables as well as with the output variable to pre-process the data. CervDetect uses the random forest (RF) feature selection technique to select significant features. Finally, CervDetect uses a hybrid approach by combining RF and shallow neural networks to detect Cervical Cancer. Results show that CervDetect accurately predicts cervical cancer, outperforms the state-of-the-art studies, and achieved an accuracy of 93.6%, mean squared error (MSE) error of 0.07111, false-positive rate (FPR) of 6.4%, and false-negative rate (FNR) of 100%.

Local Differential Privacy in the Medical Domain to Protect Sensitive Information: Algorithm Development and Real-World Validation

Background

Privacy is of increasing interest in the present big data era, particularly the privacy of medical data. Specifically, differential privacy has emerged as the standard method for preservation of privacy during data analysis and publishing.

Objective

Using machine learning techniques, we applied differential privacy to medical data with diverse parameters and checked the feasibility of our algorithms with synthetic data as well as the balance between data privacy and utility.

Methods

All data were normalized to a range between –1 and 1, and the bounded Laplacian method was applied to prevent the generation of out-of-bound values after applying the differential privacy algorithm. To preserve the cardinality of the categorical variables, we performed postprocessing via discretization. The algorithm was evaluated using both synthetic and real-world data (from the eICU Collaborative Research Database). We evaluated the difference between the original data and the perturbated data using misclassification rates and the mean squared error for categorical data and continuous data, respectively. Further, we compared the performance of classification models that predict in-hospital mortality using real-world data.

Results

The misclassification rate of categorical variables ranged between 0.49 and 0.85 when the value of ε was 0.1, and it converged to 0 as ε increased. When ε was between 10² and 10³, the misclassification rate rapidly dropped to 0. Similarly, the mean squared error of the continuous variables decreased as ε increased. The performance of the model developed from perturbed data converged to that of the model developed from original data as ε increased. In particular, the accuracy of a random forest model developed from the original data was 0.801, and this value ranged from 0.757 to 0.81 when ε was 10^-1 and 10⁴, respectively.

Conclusions

We applied local differential privacy to medical domain data, which are diverse and high dimensional. Higher noise may offer enhanced privacy, but it simultaneously hinders utility. We should choose an appropriate degree of noise for data perturbation to balance privacy and utility depending on specific situations.

Research on medical system based on blockchain technology

Medical data sharing, anti-tampering, and leakage prevention have always been severe problems that plagued the pharmaceutical industry. When a patient is referred, he often cannot provide information about previous visits because the medical information of each hospital cannot be shared in most cases, but only through Medical records, test sheets, and other easily lost paper information are used to share some medical information. At the same time, patient medical information is easily leaked, and the medical information provided in the event of a medical dispute cannot guarantee authenticity and impartiality. This article designs a consortium medical blockchain system based on a Possible Byzantine Fault Tolerance algorithm. This system is a medical system that is maintained and shared by multiple nodes and can prevent medical data from being tampered with or leaked. It can be used to solve these medical problems. Compared with the existing medical blockchain system, this system has certain advantages and better applicability.

Stochastic Channel-Based Federated Learning With Neural Network Pruning for Medical Data Privacy Preservation: Model Development and Experimental Validation

Background

Artificial neural networks have achieved unprecedented success in the medical domain. This success depends on the availability of massive and representative datasets. However, data collection is often prevented by privacy concerns, and people want to take control over their sensitive information during both the training and using processes.

Objective

To address security and privacy issues, we propose a privacy-preserving method for the analysis of distributed medical data. The proposed method, termed stochastic channel-based federated learning (SCBFL), enables participants to train a high-performance model cooperatively and in a distributed manner without sharing their inputs.

Methods

We designed, implemented, and evaluated a channel-based update algorithm for a central server in a distributed system. The update algorithm will select the channels with regard to the most active features in a training loop, and then upload them as learned information from local datasets. A pruning process, which serves as a model accelerator, was further applied to the algorithm based on the validation set.

Results

We constructed a distributed system consisting of 5 clients and 1 server. Our trials showed that the SCBFL method can achieve an area under the receiver operating characteristic curve (AUC-ROC) of 0.9776 and an area under the precision-recall curve (AUC-PR) of 0.9695 with only 10% of channels shared with the server. Compared with the federated averaging algorithm, the proposed SCBFL method achieved a 0.05388 higher AUC-ROC and 0.09695 higher AUC-PR. In addition, our experiment showed that 57% of the time is saved by the pruning process with only a reduction of 0.0047 in AUC-ROC performance and a reduction of 0.0068 in AUC-PR performance.

Conclusions

In this experiment, our model demonstrated better performance and a higher saturating speed than the federated averaging method, which reveals all of the parameters of local models to the server. The saturation rate of performance could be promoted by introducing a pruning process and further improvement could be achieved by tuning the pruning rate.

Federated Learning on Clinical Benchmark Data: Performance Assessment

Background

Federated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a significant advantage in protecting personal privacy. Therefore, many studies are being actively conducted in the applications of FL for diverse areas.

Objective

The aim of this study was to evaluate the reliability and performance of FL using three benchmark datasets, including a clinical benchmark dataset.

Methods

To evaluate FL in a realistic setting, we implemented FL using a client-server architecture with Python. The implemented client-server version of the FL software was deployed to Amazon Web Services. Modified National Institute of Standards and Technology (MNIST), Medical Information Mart for Intensive Care-III (MIMIC-III), and electrocardiogram (ECG) datasets were used to evaluate the performance of FL. To test FL in a realistic setting, the MNIST dataset was split into 10 different clients, with one digit for each client. In addition, we conducted four different experiments according to basic, imbalanced, skewed, and a combination of imbalanced and skewed data distributions. We also compared the performance of FL to that of the state-of-the-art method with respect to in-hospital mortality using the MIMIC-III dataset. Likewise, we conducted experiments comparing basic and imbalanced data distributions using MIMIC-III and ECG data.

Results

FL on the basic MNIST dataset with 10 clients achieved an area under the receiver operating characteristic curve (AUROC) of 0.997 and an F1-score of 0.946. The experiment with the imbalanced MNIST dataset achieved an AUROC of 0.995 and an F1-score of 0.921. The experiment with the skewed MNIST dataset achieved an AUROC of 0.992 and an F1-score of 0.905. Finally, the combined imbalanced and skewed experiment achieved an AUROC of 0.990 and an F1-score of 0.891. The basic FL on in-hospital mortality using MIMIC-III data achieved an AUROC of 0.850 and an F1-score of 0.944, while the experiment with the imbalanced MIMIC-III dataset achieved an AUROC of 0.850 and an F1-score of 0.943. For ECG classification, the basic FL achieved an AUROC of 0.938 and an F1-score of 0.807, and the imbalanced ECG dataset achieved an AUROC of 0.943 and an F1-score of 0.807.

Conclusions

FL demonstrated comparative performance on different benchmark datasets. In addition, FL demonstrated reliable performance in cases where the distribution was imbalanced, skewed, and extreme, reflecting the real-life scenario in which data distributions from various hospitals are different. FL can achieve high performance while maintaining privacy protection because there is no requirement to centralize the data.

Adding Sound to Medical Data

Medical data can be represented in various forms. The most common is visualization, but recent work started to also add sonic representation - sonification. In this study we start with a theoretical background, then focus on medical applications. The discussion synthesizes the authors view about the present state of the domain and tries to foresee future potential developments in medicine. In conclusion we present a set of original recommendations for developing new applications with potential use in medicine and healthcare.

A new cure rate model with flexible competing causes with applications to melanoma and transplantation data

In this article, we introduce a long-term survival model in which the number of competing causes of the event of interest follows the zero-modified geometric (ZMG) distribution. Such distribution accommodates equidispersion, underdispersion, and overdispersion and captures deflation or inflation of zeros in the number of lesions or initiated cells after the treatment. The ZMG distribution is also an appropriate alternative for modeling clustered samples when the number of competing causes of the event of interest consists of two subpopulations, one containing only zeros (cure proportion), while in the other (noncure proportion) the number of competing causes of the event of interest follows a geometric distribution. The advantage of this assumption is that we can measure the cure proportion in the initiated cells. Furthermore, the proposed model can yield greater or lower cure proportion than that of the geometric distribution when modeling the number of competing causes. In this article, we present some statistical properties of the proposed model and use the maximum likelihood method to estimate the model parameters. We also conduct a Monte Carlo simulation study to evaluate the performance of the estimators. We present and discuss two applications using real-world medical data to assess the practical usefulness of the proposed model.

Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data

Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient's clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.

Cancer Prevention Using Machine Learning, Nudge Theory and Social Impact Bond. Int J Environ Res Public Health. 2020;17. [PMID: 32012838 PMCID: PMC7037430 DOI: 10.3390/ijerph17030790]

There have been prior attempts to utilize machine learning to address issues in the medical field, particularly in diagnoses using medical images and developing therapeutic regimens. However, few cases have demonstrated the usefulness of machine learning for enhancing health consciousness of patients or the public in general, which is necessary to cause behavioral changes. This paper describes a novel case wherein the uptake rate for colorectal cancer examinations has significantly increased due to the application of machine learning and nudge theory. The paper also discusses the effectiveness of social impact bonds (SIBs) as a scheme for realizing these applications. During a healthcare SIB project conducted in the city of Hachioji, Tokyo, machine learning, based on historical data obtained from designated periodical health examinations, digitalized medical insurance receipts, and medical examination records for colorectal cancer, was used to deduce segments for whom the examination was recommended. The result revealed that out of the 12,162 people for whom the examination was recommended, 3264 (26.8%) received it, which exceeded the upper expectation limit of the initial plan (19.0%). We conclude that this was a successful case that stimulated discussion on potential further applications of this approach to wider regions and more diseases.

Hiding Decision Tree Rules in Medical Data: A Case Study

Data sharing among health organizations has become an increasingly common process, but any organization will most likely try to hide some sensitive patterns before it shares its data with others. This article focuses on the protection of sensitive patterns when we assume that decision trees will be the models to be induced. We apply a heuristic approach to hideany arbitrary rule from the derivation of a binary decision tree. The proposed hiding method is preferred over other heuristic solutions such as output disturbance or encryption methods that limit data usability, as the raw data itself can then more easily be offered for access by any third parties.

Classification of healthcare data using hybridised fuzzy and convolutional neural network

Healthcare performs a key role in the health of humans in the world. While gathering a huge amount of medical data, the problems will appear on the classification of healthcare data. In this work, a fuzzy hybridised convolutional neural network (FCNN) model is stated to guess the class of healthcare data. This model collects the information from the data set and builds the decision table based on the collected features from data sets. The attributes that are unrelated are deleted by using principal component analysis algorithm. The decision of normal and cardiac disease is described by using FCNN classifier. Using the data sets from UCI (University of California Irvine) repository the estimation of the presented model is carried on. The performance of the authors' classification technique is measured by various metrics such as accuracy, F-measure, G-mean, precision, and recall. The experimental results while compared with some of the existing machine learning methods such as probabilistic neural network, support vector machine and neural network, show the higher performance of FCNN. This model presented in this work acts as a decision support pattern in healthcare for therapeutic specialists.

Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases

An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on how a Convolutional Neural Network (CNN) detects tumor tissue in patches extracted from histology whole slide images. This is achieved using the “locally-interpretable model-agnostic explanations” methodology. Two publicly-available convolutional neural networks trained on the Patch Camelyon Benchmark are analyzed. Three common segmentation algorithms are compared for superpixel generation, and a fourth simpler parameter-free segmentation algorithm is proposed. The main characteristics of the explanations are discussed, as well as the key patterns identified in true positive predictions. The results are compared to medical annotations and literature and suggest that the CNN predictions follow at least some aspects of human expert knowledge.

Selecting Accurate Classifier Models for a MERS-CoV Dataset

The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a viral respiratory disease that is spreading worldwide necessitating to have an accurate diagnosis system that accurately predicts infections. As data mining classifiers can greatly assist in enhancing the prediction accuracy of diseases in general. In this paper, classifier model performance for two classification types: (1) binary and (2) multi-class were tested on a MERS-CoV dataset that consists of all reported cases in Saudi Arabia between 2013 and 2017. A cross-validation model was applied to measure the accuracy of the Support Vector Machine (SVM), Decision Tree, and k-Nearest Neighbor (k-NN) classifiers. Experimental results demonstrate that SVM and Decision Tree classifiers achieved the highest accuracy of 86.44% for binary classification based on healthcare personnel class. On the other hand, for multiclass classification based on city class, the decision tree classifier had the highest accuracy among the remaining classifiers; although it did not reach a satisfactory accuracy level (42.80%). This work is intended to be a part of a MERS-CoV prediction system to enhance the diagnosis of MERS-CoV disease.

Use of graph algorithms in the processing and analysis of images with focus on the biomedical data

INTRODUCTION

Image segmentation is a known problem in the field of image processing. A great number of methods based on different approaches to this issue was created. One of these approaches utilizes the findings of the graph theory.

METHODS

Our work focuses on segmentation using shortest paths in a graph. Specifically, we deal with methods of "Intelligent Scissors," which use Dijkstra's algorithm to find the shortest paths.

RESULTS

We created a new software in Microsoft Visual Studio 2013 integrated development environment Visual C++ in the language C++/CLI. We created a format application with a graphical users development environment for system Windows, with using the platform .Net (version 4.5). The program was used for handling and processing the original medical data.

CONCLUSION

The major disadvantage of the method of "Intelligent Scissors" is the computational time length of Dijkstra's algorithm. However, after the implementation of a more efficient priority queue, this problem could be alleviated. The main advantage of this method we see in training that enables to adapt to a particular kind of edge, which we need to segment. The user involvement has a significant influence on the process of segmentation, which enormously aids to achieve high-quality results (Fig. 7, Ref. 13).

Medical Data Analytics Is Not a Simple Task

Data analytics represents a new chance for medical diagnosis and treatment to make it more effective and successful. This expectation is not so easy to achieve as it may look like at a first glance. The medical experts, doctors or general practitioners have their own vocabulary, they use specific terms and type of speaking. On the other side, data analysts have to understand the task and to select the right algorithms. The applicability of the results depends on the effectiveness of the interactions between those two worlds. This paper presents our experiences with various medical data samples in form of SWOT analysis. We identified the most important input attributes for the target diagnosis or extracted decision rules and analysed their interestingness with cooperating doctors, for most promising new cut-off values or an investigation of possible important relations hidden in data sample. In general, this type of knowledge can be used for clinical decision support, but it has to be evaluated on different samples, conditions and ideally in long-term studies. Sometimes, the interaction needed much more time than we expected at the beginning but our experiences are mostly positive.

Data mining and medical world: breast cancers' diagnosis, treatment, prognosis and challenges

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area.

Uncovering patterns of technology use in consumer health informatics

Internet usage and accessibility has grown at a staggering rate, influencing technology use for healthcare purposes. The amount of health information technology (Health IT) available through the Internet is immeasurable and growing daily. Health IT is now seen as a fundamental aspect of patient care as it stimulates patient engagement and encourages personal health management. It is increasingly important to understand consumer health IT patterns including who is using specific technologies, how technologies are accessed, factors associated with use, and perceived benefits. To fully uncover consumer patterns it is imperative to recognize common barriers and which groups they disproportionately affect. Finally, exploring future demand and predictions will expose significant opportunities for health IT. The most frequently used health information technologies by consumers are gathering information online, mobile health (mHealth) technologies, and personal health records (PHRs). Gathering health information online is the favored pathway for healthcare consumers as it is used by more consumers and more frequently than any other technology. In regard to mHealth technologies, minority Americans, compared with White Americans utilize social media, mobile Internet, and mobile applications more frequently. Consumers believe PHRs are the most beneficial health IT. PHR usage is increasing rapidly due to PHR integration with provider health systems and health insurance plans. Key issues that have to be explicitly addressed in health IT are privacy and security concerns, health literacy, unawareness, and usability. Privacy and security concerns are rated the number one reason for the slow rate of health IT adoption.