Preprint Article Version 1 This version is not peer-reviewed

Data Driven Analytics for Personalized Medical Decision Making

Version 1 : Received: 3 July 2020 / Approved: 5 July 2020 / Online: 5 July 2020 (15:04:17 CEST)

A peer-reviewed article of this Preprint also exists.

Melnykova, N.; Shakhovska, N.; Gregus, M.; Melnykov, V.; Zakharchuk, M.; Vovk, O. Data-Driven Analytics for Personalized Medical Decision Making. Mathematics 2020, 8, 1211. Melnykova, N.; Shakhovska, N.; Gregus, M.; Melnykov, V.; Zakharchuk, M.; Vovk, O. Data-Driven Analytics for Personalized Medical Decision Making. Mathematics 2020, 8, 1211.

Journal reference: Mathematics 2020, 8, 1211
DOI: 10.3390/math8081211

Abstract

The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

Subject Areas

personalization; decision making; medical data; artificial intelligence; Data-driving; Big Data; Data Mining; Machine Learning

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