All articles by Isaenkova E. A.
-
2022 № 2 Predicting the development of hypertension using machine learning models in the remote cardiomonitoring subsystem
One of the tasks of personalized medicine is to build a new organizational model for providing medical care to patients, based on the selection of individual medical, diagnostic and preventive agents that are optimally suitable for the peculiarities of the body.
Modern artificial intelligence methods allow you to solve problems of this type.
Purpose. The aim of the study is to construct and apply predictive logistic regression models and decision tree using machine learning techniques to identify patients at high risk of hypertension without the need for invasive clinical procedures.
Materials and methods. A formed data set consisting of 395 patient records of Voronezh City Clinical Clinic No. 1 is used. Each record contains patient parameters: patient sex; patient age; body mass index; waist circumference; hip circumference; tobacco smoking status; alcohol use status; systolic pressure; diastolic pressure. Machine learning methods are used to build prognostic models.
Results. Two models for predicting the development of hypertension are constructed, characterized by high indicators of classification accuracy: a logistic regression model designed to calculate the patient’s individual risk (accuracy 96%), and a decision tree model designed to predict the patient’s possible disease with hypertension and explain the reasons why this disease can occur (accuracy 94%).
Findings. The expediency of using machine learning methods in constructing prognostic models for assessing the state of patients is shown, the possibility of creating a recommendation block based on the obtained models in the remote cardiomonitoring subsystem is indicated. -
2022 № 9 Identification of directions for improving the quality of medical services using SERVQUAL and IPA techniques
Assessing patients’ expectations and perceptions of health care delivery is challenging. To understand the quality of health care delivery, a study was conducted related to how patients assess the expected and perceived quality of health care delivery in a district hospital. Modern methods of measuring patient satisfaction and its interpretation using statistical analysis methods allow solving problems of this type.
P u r p o s e : development of approach to improvement of quality of medical services provision using results of measurement of satisfaction with quality of medical services provided to patients in inpatient conditions of regional level, using SERVQUAL technique; substantiation on built regression model of influence of certain aspects of patients’ perception of quality satisfaction.
M a t e r i a l s a n d m e t h o d s . The materials of the study are presented in the form of the results of a medical and sociological study of 418 patients who received medical services in one of the medical organizations at the regional level. Methods of statistical analysis of patient questionnaire results, factor, regression analysis, Importance-Performance Analysis (IPA) were used.
R e s u l t s . An analysis of the data showed that 54.3% of patients were quite satisfied with the services. A regression equation is constructed to calculate the degree of patient satisfaction depending on a number of aspects of health care delivery. According to the IPA method, a schedule of four-quadrant distributions of patient assessments has been drawn up, on the basis of which it is possible to form measures to increase patient satisfaction with medical services.
F i n d i n g s . The use of the SERVQUAL technique allows you to measure satisfaction with the quality of medical services, and the use of the IPA technique allows you to interpret it and obtain useful information regarding the quality of medical services as an element of patient feedback in the quality management system of a medical organization