English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Current clinical practice to monitor patients' health follows either regularor heuristic-based lab test (e.g. blood test) scheduling. Such practice notonly gives rise to redundant measurements accruing cost, but may even lead tounnecessary patient discomfort. From the computational perspective,heuristic-based test scheduling might lead to reduced accuracy of clinicalforecasting models. Computationally learning an optimal clinical testscheduling and measurement collection, is likely to lead to both, betterpredictive models and patient outcome improvement. We address the schedulingproblem using deep reinforcement learning (RL) to achieve high predictive gainand low measurement cost, by scheduling fewer, but strategically timed tests.We first show that in the simulation our policy outperforms heuristic-basedmeasurement scheduling with higher predictive gain or lower cost measured byaccumulated reward. We then learn a scheduling policy for mortality forecastingin the real-world clinical dataset (MIMIC3), our learned policy is able toprovide useful clinical insights. To our knowledge, this is the first RLapplication on multi-measurement scheduling problem in the clinical setting.

Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL