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A Deep Learning Approach to Predict Neonatal Encephalopathy from Electronic Health Records.

, , , , and . ICHI, page 1-7. IEEE, (2019)

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Learning Bundled Care Opportunities from Electronic Medical Records., , , , , , and . CoRR, (2017)Learning the impact of acute and chronic diseases on forecasting neonatal encephalopathy., , , , , and . Comput. Methods Programs Biomed., (2021)Learning to Identify Severe Maternal Morbidity from Electronic Health Records., , , , , and . MedInfo, volume 264 of Studies in Health Technology and Informatics, page 143-147. IOS Press, (2019)Learning bundled care opportunities from electronic medical records., , , , , , and . J. Biomed. Informatics, (2018)Telehealth Use in the COVID-19 Pandemic: A Retrospective Study of Prenatal Care., , , and . MedInfo, volume 290 of Studies in Health Technology and Informatics, page 503-507. IOS Press, (2021)A Deep Learning Approach to Predict Neonatal Encephalopathy from Electronic Health Records., , , , and . ICHI, page 1-7. IEEE, (2019)Obstetric Patients with Repetitious Hospital Location Transfers Have Prolonged Stays., , , , and . ICHI, page 1-8. IEEE, (2019)Deep learning predicts extreme preterm birth from electronic health records., , , , , and . J. Biomed. Informatics, (2019)Leveraging Electronic Health Records to Learn Progression Path for Severe Maternal Morbidity., , , , , and . MedInfo, volume 264 of Studies in Health Technology and Informatics, page 148-152. IOS Press, (2019)Predicting Length of Stay for Obstetric Patients via Electronic Medical Records., , , , , and . MedInfo, volume 245 of Studies in Health Technology and Informatics, page 1019-1023. IOS Press, (2017)