OPTIMIZING OPERATIONAL DATA COLLECTION IN A MEDICAL INSTITUTION'S PEER-TO-PEER NETWORK
DOI:
https://doi.org/10.26906/SUNZ.2023.2.142Keywords:
medical information system, feature vector, time series classification, peering network, data prioritizationAbstract
Topicality. In modern medical information systems, the volume of data (for example, physiological parameters collected from patients) is enormous, as abnormal and normal data are collected together. This leads to delays in providing care to emergency patients. To solve this problem, optimal operational data collection schemes for short and long-term forecasting are needed. At the same time, it is advisable to use prioritization and filtering of data that are continuously collected from portable sensors on patients and sent to the information system of the medical institution. The goal of this work is to develop a model of the information system of a medical institution based on a peering network using effective methods of collecting operational data. The object of research is the automated process of collecting and processing data arrays, which will reflect the state of health of a patient of a medical institution. The subject of research is methods and algorithms for collecting and processing operational data characterizing the condition of a patient in a medical institution, for the formation of a short-term prognosis. Results. New algorithms are proposed to optimize the processes of data collection and processing by introducing a criterion of urgency for patients, which will contribute to reducing the amount of data that must be transferred, reducing the waiting time for making a diagnosis. Common clinical criteria are used to assess the urgency of patients. Conclusions. A model of the information system of a medical institution is proposed, which is effective in collecting data and optimizes the order of their processing in forecasting. Achieved a reduction in the amount of medical data received from patients and the setting of the waiting time for the data required for forecasting.Downloads
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