Geltser B I, Domzhalov I G, Shakhgeldyan K I, Kuksin N S, Kokarev E A, Pak R L, Kotelnikov V N
MD, DSc, Professor, Corresponding Member of the Russian Academy of Science, Deputy Director for Science of the School of Medicine and Life Sciences; Far Eastern Federal University, 10 Village Ayaks, Island Russkiy, Vladivostok, 690922, Russia.
PhD Student, School of Medicine and Life Sciences; Far Eastern Federal University, 10 Village Ayaks, Island Russkiy, Vladivostok, 690922, Russia; Physician, Intensive Care Department, Regional Vascular Surgery Center; Primorsky Regional Clinical Hospital No.1, 57 Aleutskaya St., Vladivostok, 690091, Russia.
Sovrem Tekhnologii Med. 2024;16(4):61-72. doi: 10.17691/stm2024.16.4.07. Epub 2024 Aug 30.
Risk stratification of hospital mortality in patients with ST segment elevation myocardial infarction on the electrocardiogram is an important part of the specialized medical care provision. The systematic review presents scientific literature data characterizing the predictive value of both classical prognostic scales (GRACE, CADDILLAC, TIMI risk score for STEMI, RECORD, etc.) and new risk measurement tools developed on the basis of modern machine learning techniques. Most studies on this issue are often focused on the search for new predictors of adverse events, which allow to detail the relations between indicators of the clinical and functional status of patients and the end point of the study. Here, an important task is to develop hospital mortality prognostic algorithms characterized by explainable artificial intelligence and trusted by doctors.
心电图显示ST段抬高型心肌梗死患者医院死亡风险分层是专科医疗护理的重要组成部分。该系统评价呈现了科学文献数据，这些数据描述了经典预后量表（GRACE、CADDILLAC、STEMI的TIMI风险评分、RECORD等）以及基于现代机器学习技术开发的新风险测量工具的预测价值。关于这个问题的大多数研究通常集中在寻找不良事件的新预测因素上，这有助于详细阐述患者临床和功能状态指标与研究终点之间的关系。在此，一项重要任务是开发具有可解释人工智能且受医生信赖的医院死亡预后算法。
