Predicting Length of Stay for Obstetric Patients via Electronic Medical Records
(0) Obstetric care refers to the care provided to patients during ante-,intra-,and postpartum periods.Predicting length of stay(LOS)for these patients during their hospitalizations can assist healthcare organizations in allocating hospital resources more effectively and efficiently,ultimately improving maternal care quality and reducing costs to patients.In this paper,we investigate the extent to which LOS can be forecast from a patients medical history.We introduce a machine learning framework to incorporate a patients prior conditions(e.g.,diagnostic codes)as features in a predictive model for LOS.We evaluate the framework with three years of historical billing data from the electronic medical records of 9188 obstetric patients in a large academic medical center.The results indicate that our framework achieved an average accuracy of 49.3%,which is higher than the baseline accuracy 37.7%(that relies solely on a patients age).The most predictive features were found to have statistically significant discriminative ability.These features included billing codes for normal delivery(indicative of shorter stay)and antepartum hypertension(indicative of longer stay).
Length of Stay Electronic Health Records Obstetrics
Cheng Gao Abel N.Kho Catherine Ivory Sarah Osmundson Bradley A.Malin You Chen
Dept.of Biomedical Informatics,School of Medicine,Vanderbilt University,Nashville,TN,USA Institute for Public Health and Medicine,Northwestern University,Chicago,IL,USA School of Nursing,Vanderbilt University,Nashville,TN,USA Dept.of Obstetrics and Gynecology,School of Medicine,Vanderbilt University,Nashville,TN,USA Dept.of Biomedical Informatics,School of Medicine,Vanderbilt University,Nashville,TN,USA;Dept.of Ele
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
1019-1023
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）