Improvement of medical information system for medical institutions by Machine Learning
DOI №______
Abstract
This article is about using machine learning to improve and improve the performance of medical information systems in Ukraine.
Most medical systems technical solutions are designed to improve the internal work of the institution and little attention is paid to interacting with patients, so the work considers machine learning to maximize client access to the healthcare facility.
The article aims to identify the optimal algorithm for the selection of a medical specialist based on the client's symptoms without human involvement, as well as to improve the operation of such an algorithm.
The process of collecting and special training of the data set for machine learning of machine model is investigated. The complex work of selection of the necessary data combinations was carried out, which gave the best result in predicting the result.
The study used a one-to-many approach for many machine learning tasks, due to the particularity of the training dataset. The most optimal algorithm of machine learning with the teacher has been identified, for predicting specialist doctor based on the information of symptoms indicated by the client system, which is recorded for admission.
The method used has been refined, due to the disadvantages of high demanding data. Changes to the algorithm work to estimate the weight status of the current forecast example, increasing the performance.
The advanced method is based on the use of averaged values of weight criteria, instead of storing the entire collection of weight criteria, which increases the speed of the algorithm, and also makes it less demanding to the data set used in training the model.
Keywords: medical information systems; machine learning; perceptron; machine learning; algorithm; method; voting perceptron.
References
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