2 citations · 2 across the 5 of their papers we have counts for
5 papers
Can we generate portable representations for clinical time series data using LLMs?
Zongliang Ji, Yifei Sun, Andre Amaral +2
Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the next. In this work, we study a simple question -- can lar…
Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development
Zongliang Ji, Ziyang Zhang, Xincheng Tan +5
Evidence-based medicine (EBM) is central to high-quality care, but remains difficult to implement in fast-paced primary care settings. Physicians face short consultations, increasi…
ExOSITO: Explainable Off-Policy Learning with Side Information for Intensive Care Unit Blood Test Orders
Zongliang Ji, Andre Carlos Kajdacsy-Balla Amaral, Anna Goldenberg +1
Ordering a minimal subset of lab tests for patients in the intensive care unit (ICU) can be challenging. Care teams must balance between ensuring the availability of the right info…
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
Amin Adibi, Xu Cao, Zongliang Ji +39
The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam,…
Measurement Scheduling for ICU Patients with Offline Reinforcement Learning
Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan
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 withou…