Modeling and optimization of data collection process for artificial intelligence in medicine

Development of Artificial Intelligence technologies in medicine requires a systematic approach to collecting and processing structured datasets for training, testing, and validating machine learning models. This paper proposes a solution to this problem through simulation modeling based on queueing theory. This modeling requires estimating the planned throughput of each data collection point, ensuring a sufficient number of patients, the availability and reliability of their medical information, and meeting legal requirements regarding personal data protection and medical ethics. The proposed approach was studied using the analysis of biomedical data collection processes designed to train artificial intelligence models for remote diagnostic methods. The empirical part of the study was conducted at biomedical signal collection points over a six-month period. The total sample size was 574 patients. A simulation model was developed to optimize the data collection process. According to the simulation modeling, the average data collection intensity was 7.28 patients per day with significant variability in the workload. During the optimization process, changes were made to the data collection process through parallelization, which increased productivity by reducing the time spent on questionnaires and temperature measurements and increasing patient throughput. The optimization of the data collection process increased the workload from 4.67 to 12.12 patients per day. The proposed approach allows us to validate the architecture of the organizational and technological process for data collection before scaling and minimizes the risk of exceeding the schedule deadlines for generating medical datasets.

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