Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
This work addresses resource constraints in clinical settings by improving admission prediction, but it is incremental as it applies an existing curriculum learning paradigm to a specific domain.
The authors tackled the problem of predicting hospital inpatient admission location by proposing a student-teacher curriculum learning method via reinforcement learning, which outperformed state-of-the-art methods on tabular data and performed competitively on image recognition.
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 learning to deal with this specific problem. A representation of the weights of the student network is treated as the state and is fed as an input to the teacher network. The teacher network's action is to select the most appropriate batch of data to train the student network on from a training set sorted according to entropy. By validating on three datasets, not only do we show that our approach outperforms state-of-the-art methods on tabular data and performs competitively on image recognition, but also that novel curricula are learned by the teacher network. We demonstrate experimentally that the teacher network can actively learn about the student network and guide it to achieve better performance than if trained alone.