Data-Driven Analytics for Personalized Medical Decision Making

Mathematics

Nataliia Melnykova¹  Nataliya Shakhovska¹  Olena Vovk¹  Michal Gregus²  Volodymyr Melnykov³  Mariana Zakharchuk⁴ 

[1] Artificial Intelligence Department, Institute of Computer Sciences and Information Technologies, Lviv Polytechnic National University, 79013 Lviv, Ukraine;Department of Information Systems, Comenius University in Bratislava, 81499 Bratislava, Slovakia;General Surgery Department, Danylo Halytsky Lviv National Medical University, 79010 Lviv, Ukraine;Linguistically Education Center, Lviv Polytechnic National University, 79013 Lviv, Ukraine;

关键词: personalization;    decision making;    medical data;    artificial intelligence;    data-driven;    big data;   

DOI  :  10.3390/math8081211

来源: DOAJ

【 摘 要 】

The study was conducted by applying machine learning and data mining methods to treatment personalization. This allows individual patient characteristics to be investigated. The personalization method was built on the clustering method and associative rules. It was suggested to determine the average distance between instances in order to find the optimal performance metrics. The formalization of the medical data preprocessing stage was proposed in order to find personalized solutions based on current standards and pharmaceutical protocols. The patient data model was built using time-dependent and time-independent parameters. Personalized treatment is usually based on the decision tree method. This approach requires significant computation time and cannot be parallelized. Therefore, it was proposed to group people by conditions and to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. The novelty of the paper is the new clustering method, which was built from an ensemble of cluster algorithms, and the usage of the new distance measure with Hopkins metrics, which were 0.13 less than for the k-means method. The Dunn index was 0.03 higher than for the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm. The next stage was the mining of associative rules provided separately for each cluster. This allows a personalized approach to treatment to be created for each patient based on long-term monitoring. The correctness level of the proposed medical decisions is 86%, which was approved by experts.

【 授权许可】

Unknown