利用患者的纵向数据来改善医院的一年死亡风险
Hakima Laribi1, Nicolas Raymond1, Ryeyan Taseen2
1Department of Computer Science, Université de Sherbrooke, Sherbrooke, Canada.
Health information science and systems
|March 7, 2025
概括
预测医院死亡风险对于终身护理至关重要. 整体纵向网络 (ELN) 有效地利用患者病史来改善一年死亡率预测,帮助讨论护理目标.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
- 临床预测模型临床预测模型
背景情况:
- 准确预测中期存活率对于识别可能受益于医疗目标 (GOC) 讨论的患者至关重要.
- 利用纵向患者数据和常规入院信息可以提高死亡风险预测.
研究的目的:
- 开发和评估一个整体纵向网络 (ELN),用于预测一年的医院死亡风险.
- 评估与死亡率预测的仅入院预测指标相比,纵向患者数据的附加值.
主要方法:
- 建议使用整体纵向网络 (ELN) 来利用纵向患者记录预测一年死亡率.
- 该模型使用入院时可用的预测因素 (AdmDemo) 和包括后来的诊断 (AdmDemoDx) 进行了评估.
- 使用了123,646名患者和250,812次住院 (2011-2021) 的大型数据集,分为学习和坚持集,用于验证和临床实用性评估.
主要成果:
- 当ELN结合纵向信息时,预测性能显著改善 (p < 0.05).
- 在坚持的比赛中,ELN获得了0.83 (AdmDemo) 和0.87 (AdmDemoDx) 的AUROC.
- 精度从0.25提高到0.28 (AdmDemo) 和0.36 (AdmDemoDx),纵向数据的效用增加,与更多住院病例相关联.
结论:
- 整合纵向患者数据显著提高了医院死亡风险的预测.
- ELN可以更好地了解患者的病情和疾病严重程度,特别是当入院信息有限时.
- 改善死亡率预测有助于及时和有效地实现护理讨论的目标,以更好地管理患者.
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