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聊天GPT预测在医院所有原因的败血症死亡率:与韩国败血症联盟数据库的背景学习
Namkee Oh1, Won Chul Cha2, Jun Hyuk Seo3
1Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Healthcare informatics research
|August 20, 2024
概括
这项研究评估了ChatGPT.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 关键护理医学 关键护理医学
背景情况:
- 败血症是全球主要的死亡原因,需要准确的结果预测,以改善患者管理.
- 预测模型对于在重症监护室 (ICU) 识别高风险败血症患者至关重要.
研究的目的:
- 评估ChatGPT (GPT-3.5-turbo和GPT-4) 在预测败血症患者住院死亡率方面的疗效.
- 为了比较ChatGPT的预测性能与传统的梯度增强模型 (GBM).
主要方法:
- 利用了韩国败血症联盟 (KSA) 数据库 (2019-2021) 对成年ICU患者的数据.
- 采用结构化提示,以在不同数量的患者示例 (0-6) 进行上下文学习.
- 评估了使用AUROC等指标对7天和30天全因死亡率的预测准确性.
主要成果:
- 在7天死亡率方面,GPT-4显示出有前途的AUROC (0.70-0.83),表现优于GPT-3.5 (0.51-0.70),但与GBM (0.79) 相比.
- 对于30天死亡率,GPT-4的AUROCs在0.51-0.59之间,而GBM达到0.76.
- 对30天死亡率的零射击GPT-4预测产生了0.60s到0.75.75的AUROC.
结论:
- GPT-4显示了预测短期败血症死亡率的潜力.
- 绩效根据评估指标和预测时间框架而有所不同.
- 需要进一步的研究来优化人工智能模型用于败血症结果预测.
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