机器学习和预测模型:单中心回顾分析中SARS-CoV-2流行病的2年
Michela Rauseo1, Marco Perrini2, Crescenzio Gallo3
1Department of Anesthesia and Intensive Care Medicine, University Hospital "Policlinico Riuniti di Foggia", University of Foggia, Viale Pinto, 1, 71122, Foggia, Italy. michela.rauseo@unifg.it.
Journal of anesthesia, analgesia and critical care
|June 29, 2023
概括
优化呼吸系统支持时间和选择对于COVID-19患者的结果至关重要. 临床判断和严重性评分有助于识别有风险的患者,机器学习提供先进的评估工具.
科学领域:
- 密集护理医学 密集护理医学
- 肺部病理学 肺部病理学
- 传染性疾病 传染性疾病
背景情况:
- 自2020年1月以来,冠状病毒疾病19 (COVID-19) 在全球迅速传播.
- 早期评估疾病严重程度对于患者分层和护理至关重要.
- 对在重症监护室 (ICU) 住院的581名COVID-19患者进行了分析.
研究的目的:
- 为 COVID-19 患者的预测结果开发一个预测模型.
- 整合人口统计数据,临床病史,实验室发现和呼吸系统参数.
- 利用相关性分析和机器学习来预测结果.
主要方法:
- 分析了接受ICU治疗的成年患者.
- 收集的数据包括人口统计,病史,D-二次数,NEWS2,MEWS和PaO2/FiO2比率.
- 统计分析包括单变量,双变量和多变量方法.
主要成果:
- 死亡率与年龄正相关,HDU停留,MEWS/NEWS2,D-二次数和输管时间.
- 在PaO2/FiO2比率和非侵入性通风 (NIV) 之间观察到负相关性.
- 机器学习模型没有达到高精度;通风策略被证明是至关重要的.
结论:
- 适时提供适当的呼吸系统支持对COVID-19患者至关重要.
- 严重性评分和临床判断有效地识别高风险患者.
- 机器学习可以提高对COVID-19等复杂疾病的评估.
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