Feb. 13, 2024, 5:42 a.m. | Zongliang Ji Anna Goldenberg Rahul G. Krishnan

cs.LG updates on arXiv.org arxiv.org

Scheduling laboratory tests for ICU patients presents a significant challenge. Studies show that 20-40% of lab tests ordered in the ICU are redundant and could be eliminated without compromising patient safety. Prior work has leveraged offline reinforcement learning (Offline-RL) to find optimal policies for ordering lab tests based on patient information. However, new ICU patient datasets have since been released, and various advancements have been made in Offline-RL methods. In this study, we first introduce a preprocessing pipeline for the …

challenge cs.ai cs.lg information lab laboratory measurement offline patient patients prior reinforcement reinforcement learning safety scheduling show studies tests work

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Data Science Advisor

@ Blue Yonder | Hamburg

Data Analyst

@ Sinch | São Paulo, State of São Paulo, Brazil - Remote

Data Engineer - Híbrido

@ SGS | Callao, Peru

Senior Analytics Engineer Brazil

@ Hiflylabs | Blumenau, Hungary