评估人工智能在治疗急性胰腺炎的临床决策中的作用
Mete Ucdal1, Amir Bakhshandehpour1,2, Muhammed Bahaddin Durak1,3
1Division of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara 06800, Türkiye.
这项研究表明,谷歌双子号准确地将美国胃肠道学会2024年指导方针应用于急性胰腺炎病例,在风险分层和治疗坚持方面表现出高度准确性. 人工智能模型对治疗急性胰腺炎的临床决策支持有希望.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 急性胰腺炎 (AP) 是一种严重的疾病,需要及时诊断和治疗.
- 美国胃肠病学会2024 (ACG24) 准则为AP护理提供了一个框架.
- 评估AI是否遵守这些准则对于改善患者的治疗结果至关重要.
研究的目的:
- 通过谷歌双子模型评估ACG24指南在管理AP案件中的应用.
- 评估双子座在风险分层,营养时间和AP并发症管理方面的准确性.
- 将人工智能驱动的建议与既定的临床指南进行比较.
主要方法:
- 一项来自MIMIC-III数据库 (2001-2012) 的512名AP患者的观察性横截面研究.
- 双子座的表现与ACG24风险分层,肠道营养和瘤性胰腺炎的指南进行比较.
- 分析预测严重程度的准确性,兰森得分,以及遵守营养和抗生素协议.
主要成果:
- 双子座在轻度AP方面达到85%的准确率,在严重AP方面达到82%.
- 模型的预测与急性生理学和慢性健康评估II和朗森得分 (p>0.25) 密切匹配.
- 营养和瘤性胰腺炎管理指南的遵守率在78%到85%之间,在严重病例中,提醒过早口服建议率为8%.
结论:
- 谷歌双子模型在应用ACG24指南的AP严重程度和治疗方面表现出显著的准确性 (78-85%).
- 基于人工智能的临床决策支持系统可以提供快速,一致和符合指南的建议.
- 在严重的AP病例中,为最佳营养时间推需要进一步细化.
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