An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis
An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis
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用于脓毒症早期检测的可解释的人工智能预测器
DOI：
10.1097/ccm.0000000000004550
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发表时间：
2020-11-01
影响因子：
8.8
通讯作者：
Li, Jianqing
中科院分区：
文献类型：
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作者：
Yang, Meicheng;Liu, Chengyu;Li, Jianqing
目的：脓毒症的早期发现在临床实践中至关重要，因为每延迟治疗一小时，就会因不可逆的器官损伤而导致死亡率增加。本研究旨在通过分析 2019 年 PhysioNet/心脏病学挑战赛提供的 ICU 电子健康记录数据，开发一种可解释的人工智能模型，用于早期预测脓毒症。设计：回顾性观察研究。设置：我们在共享 ICU 公开数据上开发了我们的模型，并在完整的隐藏人群上进行了挑战评分验证。患者：公共数据库包括来自贝斯以色列女执事医疗中心（医院系统 A）和埃默里大学医院（医院系统 B）的 40,336 名患者的电子健康记录。来自医院系统 A、B 和 C（身份不明的医院系统）的总共 24,819 名患者被隔离为完全隐藏的测试集。干预措施：无。测量和主要结果：每小时总共提取 168 个特征。可解释的人工智能脓毒症预测模型经过训练可以实时预测脓毒症。深入探讨了每个特征对每小时脓毒症预测的影响，以显示其可解释性。在完整隐藏测试集上进行测试时，该算法在本次挑战中的最终临床效用得分为 0.364，三个独立测试集的得分分别为 0.430、0.422 和 -0.048。结论：可解释的人工智能脓毒症预测模型在实时预测脓毒症风险方面取得了优异的性能，并为了解 ICU 脓毒症风险提供了可解释的信息。
Objectives: Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial intelligence model for early predicting sepsis by analyzing the electronic health record data from ICU provided by the PhysioNet/Computing in Cardiology Challenge 2019. Design: Retrospective observational study. Setting: We developed our model on the shared ICUs publicly data and verified on the full hidden populations for challenge scoring. Patients: Public database included 40,336 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (hospital system A) and Emory University Hospital (hospital system B). A total of 24,819 patients from hospital systems A, B, and C (an unidentified hospital system) were sequestered as full hidden test sets. Interventions: None. Measurements and Main Results: A total of 168 features were extracted on hourly basis. Explainable artificial intelligence sepsis predictor model was trained to predict sepsis in real time. Impact of each feature on hourly sepsis prediction was explored in-depth to show the interpretability. The algorithm demonstrated the final clinical utility score of 0.364 in this challenge when tested on the full hidden test sets, and the scores on three separate test sets were 0.430, 0.422, and -0.048, respectively. Conclusions: Explainable artificial intelligence sepsis predictor model achieves superior performance for predicting sepsis risk in a real-time way and provides interpretable information for understanding sepsis risk in ICU.