mlsanityData to 7 Oct 2026

AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment

arXiv 2203.02446 ↗First posted 4 Mar 2022cs.AI cs.LGcited by 2

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Abstract (arXiv)

Given a deep learning model trained on data from a source site, how to deploy the model to a target hospital automatically? How to accommodate heterogeneous medical coding systems across different hospitals? Standard approaches rely on existing medical code mapping tools, which have significant practical limitations. To tackle this problem, we propose AutoMap to automatically map the medical codes across different EHR systems in a coarse-to-fine manner: (1) Ontology-level Alignment: We leverage the ontology structure to learn a coarse alignment between the source and target medical coding systems; (2) Code-level Refinement: We refine the alignment at a fine-grained code level for the downstream tasks using a teacher-student framework. We evaluate AutoMap using several deep learning models with two real-world EHR datasets: eICU and MIMIC-III. Results show that AutoMap achieves relative improvements up to 3.9% (AUC-ROC) and 8.7% (AUC-PR) for mortality prediction, and up to 4.7% (AUC-ROC) and 3.7% (F1) for length-of-stay estimation. Further, we show that AutoMap can provide accurate mapping across coding systems. Lastly, we demonstrate that AutoMap can adapt to the two challenging scenarios: (1) mapping between completely different coding systems and (2) between completely different hospitals.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 52 references that are in the map. First posted on arXiv 4 Mar 2022.

Builds on

28 of 52 references.

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16 more
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Citing papers per year: 2022: 2.

2 papers.

  1. 2022GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learningcited by 35
  2. 2022UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledgecited by 4