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UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge
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Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models. To address this challenge, we propose Universal Healthcare Predictive Framework (UniHPF), which requires no medical domain knowledge and minimal pre-processing for multiple prediction tasks. Experimental results demonstrate that UniHPF is capable of building large-scale EHR models that can process any form of medical data from distinct EHR systems. We believe that our findings can provide helpful insights for further research on the multi-source learning of EHRs.
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Cited by 1 Pith paper
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EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records
A single LLM fine-tuned with a learned summary bottleneck forecasts next-hour EHR states and iteratively simulates multi-hour patient trajectories across ED, ward, and ICU on MIMIC-IV.
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