Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge

Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, such as readmission at 30 days, mainly due to data s...

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Veröffentlicht in:	AMIA Summits on Translational Science proceedings 2021, Vol.2021, p.132-141
Hauptverfasser:	Chakraborty, Prithwish, Codella, James, Madan, Piyush, Li, Ying, Huang, Hu, Park, Yoonyoung, Yan, Chao, Zhang, Ziqi, Gao, Cheng, Nyemba, Steve, Min, Xu, Basak, Sanjib, Ghalwash, Mohamed, Shahn, Zach, Suryanarayanan, Parthasararathy, Buleje, Italo, Harrer, Shannon, Miller, Sarah, Rajmane, Amol, Walsh, Colin, Wanderer, Jonathan, Reed, Gigi Yuen, Ng, Kenney, Sow, Daby, Malin, Bradley A
Format:	Artikel
Sprache:	eng
Schlagworte:	Hospitals Humans Machine Learning Neural Networks, Computer Patient Discharge
Online-Zugang:	Volltext
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