mlsanityData to 7 Oct 2026

UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge

arXiv 2211.08082 ↗First posted 15 Nov 2022cs.LG cs.NEcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 15 Nov 2022.

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18 of 33 references.

References · 18

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6 more
  1. 2015Learning to Diagnose with LSTM Recurrent Neural Networkscited by 99
  2. 2019Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformercited by 88
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2024Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHRscited by 39
  2. 2023General-Purpose Retrieval-Enhanced Medical Prediction Model Using Near-Infinite Historycited by 12
  3. 2025EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Recordscited by 2
  4. 2026EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Recordscited by 0