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Predicting the deterioration of the condition of patients with cardiovascular diseases based on machine learning methods

This study was carried out as part of a project to develop a subsystem for predicting the deterioration of the condition of patients with cardiovascular diseases on the platform of the medical information system "1C: Medicine. Hospital". The relevance of this task is due to the particularly high danger of this group of diseases and the necessity to make timely decisions about hospitalization or treatment when there is a risk of deterioration of the patient’s condition. The goal of this work was to create a tool that allows the attending physician to quickly obtain a reasonable assessment of the risk of deterioration of the patient’s condition based on available medical indicators. As a part of this study, an analysis of more than 30 thousand records containing patient health indicators downloaded from the regional medical information system was performed. The data set was labeled in accordance with the available information about medical decisions made (by attending physicians at the clinic and hospital). The lack of a standardized input of health indicators into the medical system required a significant amount of work to pre-process the input data and prepare it for modeling purposes. The prepared data was used to build a predictive model applying machine learning methods. Based on the results of the computational experiments, gradient boosting was chosen as the learning algorithm; the optimal parameters of this algorithm were selected. The prediction quality of the trained models was tested on data from the labeled set that did not participate in the training process. The quality indicators of the best model on test data were precision = 0.87; recall = 0.96; ­AUC-­ROC = 0.97. The integration of trained models with the attending physician’s automated workstation in the 1C: Medicine. Hospital system was implemented. Thus, an algorithm for processing patient health indicators from downloading primary data from the medical accounting system to obtaining a forecast was developed, taking into account the peculiarities of data storage in the system and allowing the doctor to quickly receive information about identified risk cases after each update of indicator values in the system. It was shown that standardizing the values of medical research results entered into the system will help to improve the quality of forecasting by increasing the model’s stability to changes in input data. Read more...