Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures

被引:22
|
作者
Mortazavi, Bobak J. [1 ,2 ]
Desai, Nihar [1 ]
Zhang, Jing [1 ]
Coppi, Andreas [1 ]
Warner, Fred [1 ]
Krumholz, Harlan M. [1 ]
Negahban, Sahand [1 ,2 ]
机构
[1] Yale Univ, Yale Sch Med, Ctr Outcomes Res & Evaluat, New Haven, CT 06510 USA
[2] Yale Univ, Dept Stat, New Haven, CT 06510 USA
关键词
Cardiology; electronic health records; machine learning; outcomes; Prediction; ELECTRONIC MEDICAL-RECORD; POSTOPERATIVE PULMONARY COMPLICATIONS; HEART-FAILURE; ROTHMAN INDEX; RISK-FACTORS; READMISSION; MORBIDITY; SURGERY; MODELS; COST;
D O I
10.1109/JBHI.2017.2675340
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Electronic health records (EHR) provide opportunities to leverage vast arrays of data to help prevent adverse events, improve patient outcomes, and reduce hospital costs. This paper develops a postoperative complications prediction system by extracting data from the EHR and creating features. The analytic engine then provides model accuracy, calibration, feature ranking, and personalized feature responses. This allows clinicians to interpret the likelihood of an adverse event occurring, general causes for these events, and the contributing factors for each specific patient. The patient cohort considered was 5214 patients in Yale-New Haven Hospital undergoing major cardiovascular procedures. Cohort-specific models predicted the likelihood of postoperative respiratory failure and infection, and achieved an area under the receiver operating characteristic curve of 0.81 for respiratory failure and 0.83 for infection.
引用
收藏
页码:1719 / 1729
页数:11
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