Current clinical practice for monitoring patients' health follows either regular or heuristic-based lab test (e.g. blood test) scheduling. Such practice not only gives rise to redundant measurements accruing cost, but may even lead to unnecessary patient discomfort. From the computational perspective, heuristic-based test scheduling might lead to reduced accuracy of clinical forecasting models. A data-driven measurement scheduling is likely to lead to both more accurate predictions and less measurement costs. We address the scheduling problem using deep reinforcement learning (RL) and propose a general and scalable framework to achieve high predictive gain and low measurement cost, by scheduling fewer, but strategically timed tests. Using simulations we show that our policy outperforms heuristic-based measurement scheduling having higher predictive gain and lower cost. We then learn a scheduling policy for mortality forecasting in the real-world clinical dataset (MIMIC3). Our policy decreases the total number of measurements by $31\%$ without reducing the predictive performance, or improves $3$ times more predictive gain with the same number of measurements.
Chun-Hao (Kingsley) Chang (University of Toronto)
Mingjie Mai (University of Toronto)
Anna Goldenberg (University of Toronto)
Related Events (a corresponding poster, oral, or spotlight)
2019 Poster: Dynamic Measurement Scheduling for Event Forecasting using Deep RL »
Wed Jun 12th 06:30 -- 09:00 PM Room Pacific Ballroom