Student-teacher curriculum learning via reinforcement learning: predicting hospital inpatient admission location
Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department. In this work we propose a student-teacher network via reinforcement l...
Main Authors: | el-Bouri, R, Eyre, D, Watkinson, P, Zhu, T, Clifton, DA |
---|---|
Format: | Conference item |
Language: | English |
Published: |
PMLR
2020
|
Similar Items
-
Hospital admission location prediction via deep interpretable networks for the year-round improvement of emergency patient care
by: el-Bouri, R, et al.
Published: (2020) -
Deep reinforcement learning for multi-class imbalanced training: applications in healthcare
by: Yang, J, et al.
Published: (2023) -
Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge
by: Bishop, J, et al.
Published: (2021) -
Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning
by: Yang, J, et al.
Published: (2023) -
Locating the Child in the Curriculum: Teachers' Conceptions of Student Individuality
by: Steven Katz, et al.
Published: (2006-12-01)