https://github.com/asappresearch/kbc-pomr
Code for the paper "Knowledge Base Completion for Constructing Problem-Oriented Medical Records" at MLHC 2020
https://github.com/asappresearch/kbc-pomr
Last synced: 3 days ago
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Code for the paper "Knowledge Base Completion for Constructing Problem-Oriented Medical Records" at MLHC 2020
- Host: GitHub
- URL: https://github.com/asappresearch/kbc-pomr
- Owner: asappresearch
- License: mit
- Created: 2020-07-23T17:48:09.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-06-08T21:51:25.000Z (almost 4 years ago)
- Last Synced: 2025-04-05T11:11:12.313Z (27 days ago)
- Language: Python
- Homepage:
- Size: 1.05 MB
- Stars: 11
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# kbc-pomr
Code for the [paper](https://arxiv.org/abs/2004.12905) "Knowledge Base Completion for Constructing Problem-Oriented Medical Records" at MLHC 2020
# Data
All annotations can be found at `data/all.csv`.
Each row lists a problem, a relation type (also the data type of the target), and the target code, along with the annotated label (1 = negative, 2 = positive).
In the `data/` directory, we also have the `train`, `dev`, and `test` splits for both experiments conducted in the paper.
`*_probs.csv` files contains data splits, separated by problem type (Table 3), and `*_rand.csv` files contains data splits, separated at random (Table 2).
We also provide:
- `data/med_may_treat.csv` - An auxiliary lookup to find the SNOMED/ICD diagnosis codes that an RxNorm code may be related to, which we constructed by going through NDF-RT's "MayTreat" and "MayPrevent" relations
- `data/problem_codes_all.csv` - A file with our problem definitions
- `data/site_icd9_relative_freqs.csv` - A file with the relative frequencies of ICD-9 codes computed from our EHR dataset, to properly initialize problem embeddings
- `intersect_*.txt`: the lists of codes for each data type that we evaluate on, which we constructed by taking the intersection with the set of site-specific codes.
- `vocab.txt` - the vocabulary used (site-specific codes censored with X's)
- `embeddings/claims_codes_hs_300.txt` and `embeddings/claims_cuis_hs_300.txt` - the code and CUI embeddings from Choi et al
# Reproduction of results
## Download open-source embeddings
First, download and extract (with `gunzip`) the embeddings for codes ([here](https://github.com/clinicalml/embeddings/blob/master/claims_codes_hs_300.txt.gz)) and CUIs ([here](https://github.com/clinicalml/embeddings/blob/master/claims_cuis_hs_300.txt.gz)) from prior work, and put the files in the `embeddings/` directory.
## Environment setup
To set up the proper dependencies using conda, run:
`conda create -n POMR python=3.7`
`conda activate POMR`
`pip install -r requirements.txt`
## Reproducing experiments
The jupyter notebook `Reproduction.ipynb` gives full instructions to reproduce the results from the paper, specifically line 4 ("Choi et al") in Table 2 and lines 1 ("Ontology baseline") and 5 ("Choi et al") in Table 3.
At a high level, the steps are:
- Construct RxNorm-to-CUI lookup using UMLS, so we can use Choi et al's medication embeddings
- Pre-compute problem and target embeddings to use to initialize models.
- Train on the held-out triplets data splits (`*_rand.csv`) to reproduce Table 2
- Train on the held-out problems data splits (`*_newprobs.csv`) to reproduce Table 3
# Citation
If you use this repository, please cite our paper:
```
@inproceedings{mullenbach2020knowledge,
title={Knowledge Base Completion for Constructing Problem-Oriented Medical Records},
author={Mullenbach, James and Swartz, Jordan and McKelvey, T Greg and Dai, Hui and Sontag, David},
booktitle={Machine Learning for Healthcare Conference},
year={2020}
}
```