在临床护理的各个阶段计算衍生过渡点
Aidan Gilson1, David Chartash2,3, R Andrew Taylor4,2
1Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA. aidangilson@gmail.com.
NPJ digital medicine
|June 11, 2024
概括
这项研究使用了统计方法,以找到关键生命阶段,患者多发病变化显著变化. 机器学习发现了超越传统儿科和老年护理的新临床阶段.
科学领域:
- * 医疗信息学 医疗信息学
- * 生物统计学
- * 老年学是一门学科.
背景情况:
- * 患者护理阶段传统上以年龄来定义,而不是临床负担.
- * 多种疾病负担在整个生命过程中发生显著变化.
- * 传统的基于年龄的阶段可能不反映临床现实.
研究的目的:
- * 确定患者多病症负担中的过渡点.
- *根据数据驱动的洞察力来定义新的临床阶段.
- * 为了比较数据驱动的过渡与传统护理阶段.
主要方法:
- *对520万例患者接触 (2013-2022) 的回顾性队列分析.
- *使用集群分析,假近邻 (FNN) 和过渡性分析.
- * 使用无监督机器学习来精确确定临床阶段过渡.
主要成果:
- *在2,17,41,66岁时集群确定过渡,与儿科,成人和老年阶段保持一致.
- * FNN和过渡性分析揭示了20-40岁之间的许多过渡,挑战了同质性假设.
- *在3至17岁之间发现了很少的转变,这表明持续的多病症负担.
结论:
- *无监督机器学习可以识别多病症负担的临床显著变化.
- * 数据驱动的过渡点提供了更准确的临床阶段表示.
- *确定过渡可能需要重新评估传统的儿科和老年护理模式.
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