使用基于机器学习的选工具进行营养不良风险评估:一个多中心的回顾性队列
Prathamesh Parchure1, Melanie Besculides1,2, Serena Zhan1,2
1Icahn School of Medicine at Mount Sinai, New York, New York, USA.
概括
一个机器学习工具 (MUST-Plus) 能够在医院早期有效地识别营养不良患者. 这提高了营养不良的诊断和记录率,有助于及时干预,并降低了医疗保健成本.
科学领域:
- 临床营养学 临床营养学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 营养不良显著增加了患者的发病率,死亡率和医疗保健费用.
- 早期识别营养不良对于有效及时的患者干预至关重要.
- 本研究评估了机器学习查工具对医院营养不良检测的影响.
研究的目的:
- 评估MUST-Plus机器学习工具在早期营养不良识别方面的有效性.
- 评估该工具对改善营养不良诊断和记录率的影响.
- 确定注册营养学家 (RD) 对MUST-Plus工具的可用性和接受性.
主要方法:
- 在一个大型城市卫生系统的六家医院进行了一项回顾性队列研究.
- 该研究包括成人患者 (≥18岁) 住院长度≤30天,不包括COVID-19入院患者.
- 分析了7736次住院的数据,以比较MUST-Plus实施前后的营养不良查.
主要成果:
- 通过RD评估,MUST-Plus通过RD评估确定了25.2% (1947/7736) 的住院治疗为营养不良.
- 实施MUST-Plus减少了入院和营养不良诊断之间的时间延迟.
- 该工具在RDs中显示出高可用性 (>90%),观察到诊断和记录率有所改善.
结论:
- 机器学习工具MUST-Plus在改善住院患者的营养不良查方面显示出显著的前景.
- 有效实施需要足够的研发人员和对该工具的培训.
- 其他卫生系统可以利用EHR数据来开发类似的基于ML的流程,以更好地照顾营养不良.
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