Эффективные системы поддержки принятия решений в клинической практике и профилактике: обзор литературы

Комков А.А., Рязанова С.В., Мазаев В.П. Эффективные системы поддержки принятия решений в клинической практике и профилактике: обзор литературы // CardioСоматика. 2023. Т. 14, № 3. С. 177–185. DOI: https://doi.org/10.17816/CS569263

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Komkov AA, Ryazanova SV, Mazaev VP. Effective decision support systems in clinical practice and prevention: literature review. Cardiosomatics. 2023;14(3):177–185. DOI: https://doi.org/10.17816/CS569263

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Аннотация

Системы принятий клинических решений (СПКР) способны в значительной степени упростить работу специалистов и помочь избежать врачебных ошибок, часто значимо превосходя человеческие возможности в обработке большого количества информации. Внедрение подобных систем представляет собой сложную задачу и нуждается в высокотехнологичных разработках. Годовой прирост создания таких систем представляет собой геометрическую прогрессию, однако вопрос внедрения большинства из них в реальную клиническую практику и клинические рекомендации остается открытым. СПКР демонстрируют разнообразие их использования для решения разных вопросов диагностики, лечения и профилактики заболеваний, а также рассматривают связь между научными клиническими наблюдениями. В настоящее время технологические возможности создания СПКР используют многие системы накопления и обработки данных с применением алгоритмов машинного обучения и свёрточных нейронных сетей, что приводит к получению данных, опережающих способности человеческого мышления принять логику рекомендуемых решений. В работе представлены наиболее изученные современные СПКР, возможности их применения и проблемы внедрения.

Ключевые слова: системы поддержки принятия врачебных решений; системы принятия клинических решений; машинное обучение

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Clinical decision support systems (CDSS) often outperform human capabilities for processing a large amount of information, dramatically simplifying the work of specialists and avoiding medical errors. The implementation of such systems is a complex task that requires high-tech developments. The annual increase in the development of such systems has a geometric progression. However, it is unclear if most of them will be integrated into clinical practice and recommendations. The use of CDSS to address various disease diagnosis, treatment, and prevention issues is demonstrated, and possible linkages between scientific clinical observations and CDSS are examined. Currently, many data gathering and processing systems use machine learning algorithms and convolutional technologies to create CDSS, resulting in data that exceeds the ability of human thinking to determine the logic of recommended decisions. This study presents the most studied modern CDSS, the possibilities of their application, and the implementation issues.

Keywords: clinical decision support system; machine learning

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15. Alasiri SF, Douiri A, Altukistani S, et al. The Role of Clinical Decision Support Systems in Preventing Stroke in Primary Care: A Systematic Review. Perspect Health Inf Manag. 2023;20(2):1d.
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19. Lewis G, Sharp D, Bartholomew J, Pelosi AJ. Computerized assessment of common mental disorders in primary care: effect on clinical outcome. Fam Pract. 1996;13(2):120–126. doi: 10.1093/fampra/13.2.120
20. Wellwood J, Johannessen S, Spiegelhalter DJ. How does computer-aided diagnosis improve the management of acute abdominal pain? Ann R Coll Surg Engl. 1992;74:40–46.
21. Cannon DS, Allen SN. A comparison of the effects of computer and manual reminders on compliance with a mental health clinical practice guideline. J Am Med Inform Assoc. 2000;7(2):196–203. doi: 10.1136/JAMIA.2000.0070196
22. Selker HP, Beshansky JR, Griffith JL, et al. Use of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia. A multicenter, controlled clinical trial. Ann Intern Med. 1998;129(11):845–855.
doi: 10.7326/0003-4819-129-11_part_1-199812010-00002
23. Schriger DL, Gibbons PS, Langone CA, et al. Enabling the diagnosis of occult psychiatric illness in the emergency department: A randomized, controlled trial of the computerized, self-administered PRIME-MD diagnostic system. Ann Emerg Med. 2001;37(2):132–140. doi: 10.1067/mem.2001.112255
24. Pozen MW, D’Agostino RB, Selker HP, et al. A Predictive Instrument to Improve Coronary-Care-Unit Admission Practices in Acute Ischemic Heart Disease. N Engl J Med. 2010;310(20):1273–1278. doi: 10.1056/NEJM198405173102001
25. Gijsberts CM, Den Ruijter HM, De Kleijn DPV, et al. Hematological Parameters Improve Prediction of Mortality and Secondary Adverse Events in Coronary Angiography Patients: A Longitudinal Cohort Study. Medicine (Baltimore). 2015;94(45):e1992. doi: 10.1097/MD.0000000000001992
26. Kofink D, Muller SA, Patel RS, et al. Routinely measured hematological parameters and prediction of recurrent vascular events in patients with clinically manifest vascular disease. PLoS One. 2018;13(9):e0202682. doi: 10.1371/journal.pone.0202682
27. Gijsberts CM, Ellenbroek GHJM, Ten Berg MJ, et al. Routinely analyzed leukocyte characteristics improve prediction of mortality after coronary angiography. Eur J Prev Cardiol. 2016;23(11):1211–1220. doi: 10.1177/2047487315621832
28. den Harder AM, de Jong PA, de Groot MCH, et al. Commonly available hematological biomarkers are associated with the extent of coronary calcifications. Atherosclerosis. 2018;275:166–173. doi: 10.1016/j.atherosclerosis.2018.06.017
29. Mazaev VP, Komkov AA, Ryazanova SV, et al. The identification of latent atrial fibrillation, decreased ejection fraction of the left ventricle and potassium metabolism disorders on the conventional electrocardiogram using deep convolutional neural network as an element of artificial intelligence. Modern problems of science and education. (In Russ). 2020;6.
doi: 10.17513/spno.30478

Авторы

А.А. Комков*^1,2, С.В. Рязанова¹, В.П. Мазаев¹

¹НМИЦ терапии и профилактической медицины, Москва, Российская Федерация;
²Городская клиническая больница № 67 им. Л.А. Ворохобова, Москва, Российская Федерация
*artemkomkov@gmail.com

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Artem A. Komkov*^1,2, Svetlana V. Ryazanova¹, Vladimir P. Mazaev¹

¹National Medical Research Center of Therapy and Preventive Medicine, Moscow, Russian Federation;
²Vorokhobov City Clinical Hospital N 67, Moscow, Russian Federation
*artemkomkov@gmail.com

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