住院COVID-19患者的药物使用模式和临床结果:一个地理空间和机器学习方法
Dhruva Kumar Sharma1, Madhab Nirola2, Mousumi Gupta3
1Department of Pharmacology, Sikkim Manipal Institute of Medical Sciences, Sikkim Manipal University, Gangtok, Sikkim.
The Indian journal of medical research
|June 19, 2025
概括
这项研究分析了COVID-19患者的数据,发现甲基普雷迪尼索隆的使用与较高的死亡率相关. 机器学习确定了影响患者结果的关键因素,以及死亡率的显著空间模式.
科学领域:
- 传染性疾病 传染性疾病
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
背景情况:
- 冠状病毒疾病 (COVID-19) 管理面临挑战,原因是缺乏确定的治疗指南.
- 严重急性呼吸道综合征冠状病毒2 (SARS-CoV-2) 需要研究有效的临床策略.
- 了解药物使用和患者的结果对于改善COVID-19护理至关重要.
研究的目的:
- 分析COVID-19患者的药物使用模式.
- 确定影响临床结果的因素,包括死亡率.
- 为了探索COVID-19死亡率的地理空间模式.
主要方法:
- 380名COVID-19患者的回顾性研究 (2021年4月至6月).
- 数据收集包括人口统计,药物,并发症,结果和地理空间数据.
- 机器学习模型 (随机森林) 用于分析.
主要成果:
- 随机森林模型实现了90.7%的准确性和0.86的AUROC得分.
- 甲基普雷尼索隆的使用与11.4%的死亡率有关.
- 观察到死亡率的显著空间聚类,特别是在不同性别的特定地区.
结论:
- 机器学习有效地识别了与COVID-19死亡率相关的因素.
- 甲基普雷迪尼索隆的使用和地理空间因素是患者结局的重要考虑因素.
- 建议进行进一步的前性研究来验证这些发现.
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