An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.
An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.
复制标题
DOI：
10.1097/ccm.0000000000002936
复制
发表时间：
2018-04
影响因子：
8.8
通讯作者：
Buchman TG
中科院分区：
文献类型：
--
作者：
Nemati S;Holder A;Razmi F;Stanley MD;Clifford GD;Buchman TG
败血症是危重患者发病率、死亡率和费用超支的主要原因之一。早期抗生素干预可提高脓毒症患者的生存率。然而，没有临床验证的系统存在实时预测脓毒症发作。我们的目的是开发和验证人工智能脓毒症专家（AISE）算法，用于脓毒症的早期预测。观察性队列研究。2013年1月至2015年12月的学术医疗中心。埃默里大学两家医院超过31，000名重症监护室（ICU）入院患者（开发队列），以及来自公开的MIMIC-III ICU数据库的52，000多名ICU患者（验证队列）。在ICU入院前或入院后4小时内符合脓毒症第三版国际共识定义（脓毒症-3）的患者被排除，导致我们的开发和验证队列中分别有大约27，000和42，000例患者。未提取高分辨率生命体征时间序列和电子病历（EMR）数据。每小时计算一组65个特征（变量），并传递给AISE算法，以预测在接下来的T小时（其中T = 12、8、6或4）内脓毒症的发作。AISE被用来预测在接下来的T小时内脓毒症的发作，并产生一个最重要的促成因素的列表。对于脓毒症的12小时、8小时、6小时和4小时提前预测，AISE实现了0.83 - 0.85范围内的受试者工作特征下的面积（AUROC）。AISE在开发和验证队列中的性能无法区分。使用ICU中实时可用的数据，AISE可以在临床识别前4至12小时准确预测ICU患者的脓毒症发作。一项前瞻性研究是必要的，以确定所提出的脓毒症预测模型的临床效用。
Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert (AISE) algorithm for early prediction of sepsis. Observational cohort study. Academic medical center from January 2013 to December 2015. Over 31,000 admissions to the intensive care units (ICUs) at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available MIMIC-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. None High-resolution vital signs time series and Electronic Medical Record (EMR) data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the AISE algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6 or 4). AISE was used to predict onset of sepsis in the proceeding T hours, and to produce a list of the most significant contributing factors. For the 12-hour, 8-hour, 6-hour, and 4-hour ahead prediction of sepsis, AISE achieved area under the receiver operating characteristic (AUROC) in the range of 0.83–0.85. Performance of the AISE on the development and validation cohorts were indistinguishable. Using data available in the ICU in real-time, AISE can accurately predict the onset of sepsis in an ICU patient 4 to 12 hours prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed sepsis prediction model.