  Current clinical practice to monitor 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. Computationally learning an optimal clinical test
scheduling and measurement collection, is likely to lead to both, better
predictive models and patient outcome improvement. We address the scheduling
problem using deep reinforcement learning (RL) to achieve high predictive gain
and low measurement cost, by scheduling fewer, but strategically timed tests.
We first show that in the simulation our policy outperforms heuristic-based
measurement scheduling with higher predictive gain or lower cost measured by
accumulated reward. We then learn a scheduling policy for mortality forecasting
in the real-world clinical dataset (MIMIC3), our learned policy is able to
provide useful clinical insights. To our knowledge, this is the first RL
application on multi-measurement scheduling problem in the clinical setting.
