Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model

Healthcare and medicine are key areas where machine learning algorithms are widely used. The medical decision support systems thus created are accurate enough, however, they suffer from the lack of transparency in decision making and shows a black box behavior. However, transparency and trust are significant in the field of health and medicine and hence, a black box system is sub optimal in terms of widespread applicability and reach. Hence, the explainablility of the research make the system reliable and understandable, thereby enhancing its social acceptability. The presented work explores a thyroid disease diagnosis system. SHAP, a popular method based on coalition game theory is used for interpretability of results. The work explains the system behavior both locally and globally and shows how machine leaning can be used to ascertain the causality of the disease and support doctors to suggest the most effective treatment of the disease. The work not only demonstrates the results of machine learning algorithms but also explains related feature importance and model insights.

Information technologies’ role in optimizing the application of drugs in clinical practice: a view of a clinical pharmacologist

Currently, the development and implementation of computerized medical decision support systems (CMDSS) is an effective tool aimed at optimizing drug therapy in clinical practice. It has been proven in clinical studies their use can increase the efficacy and safety of pharmacotherapy for a number of socially significant diseases. An active integration of CMDSS into medical information systems of medical organizations is required.

Applied aspects of data mining for decision support at the regional health system

The article presents the results of research of Data Mining methods with Microsoft SQL Server. Microsoft Clustering algorithm was used for improving the effectiveness of medical prevention and treatment in a cohort of patients with arterial hypertension. There are rationales for monitoring of cardiovascular risk and desire to correct the risk with Data Mining at medical decision support systems. Authors used medical and sociological monitoring data from regional clinical hospital. The segmentation of arterial hypertension patients was performed using Microsoft Clustering algorithm. As a result, a quantitative assessment of the population profile for patients with arterial hypertension was obtained. The authors presented diagrams and profiles of clusters. They were compared. The developed approach is applied for decision support at regional health information management system for reduce of cardiovascular risk.

Control model of parallel functioning production modules as fuzzy Petri nets

The article presents the results of research of Data Mining methods with Microsoft SQL Server. Microsoft Clustering algorithm was used for improving the effectiveness of medical prevention and treatment in a cohort of patients with arterial hypertension. There are rationales for monitoring of cardiovascular risk and desire to correct the risk with Data Mining at medical decision support systems. Authors used medical and sociological monitoring data from regional clinical hospital. The segmentation of arterial hypertension patients was performed using Microsoft Clustering algorithm. As a result, a quantitative assessment of the population profile for patients with arterial hypertension was obtained. The authors presented diagrams and profiles of clusters. They were compared. The developed approach is applied for decision support at regional health information management system for reduce of cardiovascular risk.

The Good, the Bad and the Ugly in App Diagnosis: Outcomes and Implications by Example

"The Clinical Decision Support (CDS), a form of artificial intelligence (AI), consider physician expertise and cognitive function along with patient’s data as the input and case-specific medical decision as an output. The improvements in physician’s performances when using a CDS ranges from 13% to 68%. The AI applications are of large interest nowadays, and a lot of effort is also put in the development of IT applications in healthcare. Medical decision support systems for non-medical staff users (MDSS-NMSF) as phone applications are nowadays available on the market. A MDSS-NMSF app is generally not accompanied by a scientific evaluation of the performances, even if they are freely available or not. Two clinical scenarios were created, and Doctor31 retrieved the diagnosis decisions. First scenario: man, 29 years old, and three symptoms: dysphagia, weight loss (normal body mass index), and tiredness. Second scenario: women, 47 years old with L5-S1 disk herniation, abnormal anti-TPO antibodies, lower back pain (burning sensations), constipation, and tiredness. The outcome possible effects and implications, as well as vulnerabilities induced on the used, are highlighted and discussed. "