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人工智能可以被用来预测实时的不良结果在个人到达急诊室与高血糖危机? :对APRN实践的影响
Alisha Amin Bhimani1, Tova Safier Frenkel, Adam Kaizer Hasham
1Author Affiliations: Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia (Drs Bhimani and Frenkel); Swedish Health Services, Seattle, Washington (Dr Bhimani); Emory Hospital, Atlanta, Georgia (Dr Frenkel); and Georgia Institute of Technology, Atlanta (Mr Hasham).
人工智能准确地预测了急诊室患有高血糖危机的患者的不良结果. 这种人工智能工具提供了一种比传统评分更有效的方法来预测诸如败血症和死亡率等关键事件.
科学领域:
- 紧急医疗 紧急医疗
- 临床信息学 临床信息学
- 医疗保健中的人工智能
背景情况:
- 紧急部门的高血糖危机对不良结果构成重大风险.
- 目前对这些患者不良事件的预测方法有局限性.
- 需要先进的工具来改善紧急护理中的患者管理.
研究的目的:
- 评估人工智能 (AI) 在预测急诊室高血糖危机患者不良结果方面的有效性.
- 将基于人工智能的预测与传统的高血糖危机死亡得分进行比较.
- 确定AI在实时临床决策支持方面的潜力.
主要方法:
- 进行了一项随机对照试验,将AI预测与传统评分系统进行比较.
- 这项研究的重点是患者出现在急诊室的高血糖危机.
- 分析的不良结果包括败血症/败血性休克,ICU入院和全因死亡率.
主要成果:
- 人工智能在实时预测不良结果方面表现出有效性.
- 人工智能的预测能力与传统的高血糖危机死亡率进行了比较.
- 该研究确定了人工智能可以帮助避免的特定不良事件.
结论:
- 人工智能显示出作为预测急诊室高血糖危机患者不良结果的实用工具的前景.
- 与传统的评分方法相比,人工智能可能具有更高的临床效用.
- 实施人工智能可能会导致更好的管理,并可能减少这种患者群体的死亡率.
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