基于实验室数据的疾病预测机器学习

Q3 Medicine
A. Gusev, R. Novitskiy, A. Ivshin, A. A. Alekseev
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引用次数: 5

摘要

目的:综述国内外关于机器学习方法在医疗信息系统(MIS)中的应用的文献,分析所研究技术的准确性和效率、优缺点以及在临床实践中实施的可能性。材料和方法。文献检索在PubMed/MEDLINE数据库中进行,涵盖2000年至2020年(使用关键字组:“机器学习”、“实验室数据”、“临床事件”、“预测疾病”)、CyberLeninka(“机器学习”、“实验室数据”、“临床事件”、“预测疾病”俄语关键字组合)和带代码的论文(“临床事件”、“预测疾病”、“电子健康记录”)。在审查了符合选择标准的30篇文献来源的全文后,选择了19篇最相关的文章。对描述人工智能技术用于获得预测分析的应用的来源进行了分析,同时考虑到有关患者的信息,如人口统计、失忆和实验室数据、仪器研究数据、管理信息系统中现有和以前疾病的信息。考虑了使用机器学习方法预测不良医疗结果的现有方法。介绍了所使用的实验室数据对构建高精度预测数学模型的意义。在MIS中实现机器学习算法似乎是一种有效预测不良医疗事件的有前途的工具,可以在实际临床实践中广泛应用。它符合基于个体风险计算的个性化医疗发展的全球趋势。在利用人工智能技术预测非传染性疾病领域的研究活动有所增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning based on laboratory data for disease prediction
Objective: to review domestic and foreign literature on the issue of machine learning methods applied in medical information systems (MIS), to analyze the accuracy and efficiency of the technologies under study, their advantages and disadvantages, the possibilities of implementation in clinical practice.Material and methods. The literature search was performed in the PubMed/MEDLINE databases covering the period from 2000 to 2020 (using groups of keyphrases: "machine learning", "laboratory data", "clinical events", "prediction diseases"), CyberLeninka ("machine learning", "laboratory data", "clinical events", "prediction diseases" Russian keyphrases combinations) and Papers With Code ("clinical events", "prediction diseases", "electronic health record"). After reviewing the full text of 30 literature sources that met the selection criteria, the 19 most relevant articles were selected.Results. An analysis of sources that describe the application of artificial intelligence techniques to obtain predictive analytics, taking into account information about patients, such as demographic, anamnestic, and laboratory data, the data of instrumental studies, information about existing and former diseases available in MIS, was performed. The existing ways of predicting adverse medical outcomes using machine learning methods were considered. Information about the significance of the used laboratory data for constructing high-precision predictive mathematical models is presented.Conclusion. Implementation of machine learning algorithms in MIS seems to be a promising tool for effective prediction of adverse medical events for wide application in real clinical practice. It corresponds to the global trend in the development of personalized medicine based on the calculation of individual risk. There is an increase in the activity of research in the field of predicting noncommunicable diseases using artificial intelligence technologies.
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来源期刊
Farmakoekonomika
Farmakoekonomika Medicine-Health Policy
CiteScore
1.70
自引率
0.00%
发文量
43
审稿时长
8 weeks
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