license: apache-2.0
language: en
tags:
- medical
- clinical-coding
- icd-10-cm
- snomed-ct
- entity-linking
- learning-to-rank
TermMap-DX: trained artifacts
Trained models for TermMap-DX, an engine that maps clinical terms extracted from charts to
one ICD-10-CM code and one SNOMED CT concept (HiLabs Hackathon 2026). The code lives in the team's
GitHub repository, Shresth-Negi/TermMap-DX (private;
judges have access). There, scripts/download_artifacts.py pulls these files into place, and
scripts/run_all.sh does so automatically before mapping an input file:
git clone https://github.com/Shresth-Negi/TermMap-DX.git && cd TermMap-DX
# set up the environment as in the README, put the challenge files in data/raw/, then
bash scripts/run_all.sh <input.csv> outputs/predictions.csv # downloads these artifacts first
Contents
| files | what |
|---|---|
ce_ICD10CM_d3e4/, ce_SNOMEDCT_US_d3e4/ |
cross-encoders fine-tuned from cambridgeltl/SapBERT-from-PubMedBERT-fulltext (4 epochs, listwise loss over candidate shortlists) |
ranker_<system>_s1<set>.txt, ranker_<system>_s2<set>.txt (+ .json feature lists) |
LightGBM LambdaRank models, stage 1 and stage 2, per coding system |
calib_<system><set>.json |
isotonic calibration (ranker margin -> P(correct)) |
config<set>.json |
model-set configuration: encoders, cross-encoder, LLM, fallback set |
emb_<encoder>_<system>.npy |
float16 embeddings of every unique dictionary description (row = description id rebuilt by the code from the supplied dictionaries) for SapBERT, gte-base and Qwen3-Embedding-0.6B |
manifest.json |
sha256 and size of every file |
Model sets: _d3llm14e4 (default; adds Qwen2.5-14B-Instruct scores at inference), _d3llm7e4
(Qwen2.5-7B-Instruct), _d3ce4 (no LLM), _sap4 (SapBERT only, lightest). The LLMs and encoders
are not included; they are pulled from their public Hugging Face repositories.
Results (5-fold cross-validation on the training file)
| set | ICD-10-CM exact match | SNOMED CT exact match |
|---|---|---|
_d3llm14e4 |
0.708 | 0.582 |
_d3llm7e4 |
0.702 | 0.567 |
_d3ce4 |
0.684 | 0.543 |
_sap4 |
0.658 | 0.507 |
Training data and licences
Trained on the HiLabs hackathon training file (proprietary; not included here). The supplied ICD-10-CM and SNOMED CT US dictionaries define the answer space; the embedding matrices are derived from their descriptions, so use is subject to the SNOMED CT licence terms that apply to you. Base models: SapBERT (Apache-2.0), gte-base (MIT), Qwen3-Embedding-0.6B (Apache-2.0), Qwen2.5-7B/14B-Instruct (Apache-2.0, used at inference only).