MedType is a BERT-based entity disambiguation module which can be incorporated with an any existing medical entity linker for enhancing its performance. For a given input text, MedType takes in the set of identified mentions along with their list of candidate concepts as input. Then, for each mention MedType predicts its semantic type based on its context in the text. The identified semantic type is utilized to disambiguate extracted mentions by filtering the candidate concepts. The figure below summarizes the entire process. The results demonstrate that MedType achieves state-of-the-art performance for medical entity linking task. Please refer to the paper for more details.
We make the following resources available in this repository:
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medtype-as-service is inspired by bert-as-service which provides a scalable implementation of BERT model for encoding thousands of documents in seconds. medtype-as-service on similar lines helps to scale MedType by serving a pretrained MedType model through an API. Basically, medtype-as-service takes in a list of variable-length text and returns entity linking output in the following form:
Input: ["Symptoms of common cold includes cough, fever, high temperature and nausea."] Output: [ { "text": "Symptoms of common cold includes cough, fever, high temperature and nausea.", "mentions":[ { "mention": "Surface form of mention", "start_offset": "Character offset indicating start of the mention", "end_offset": "Character offset indicating end of the mention", "predicted_type": ["List containing predicted semantic types for the mention"], "candidates": ["Contains list of [CUI, Score] pairs given by base entity linker"], "filtered_candidates": ["Contains MedType output: filtered list of [CUI, Score] pairs based on mention's predicted semantic types"] }, {} ] } ]
- We provide three pre-trained models for tackling different domain:
- General text (trained on WikiMed)
- Bio-Medical Research Articles (trained on WikiMed+PubMedDS+Annotated PubMed abstracts)
- Electronic Health Records (EHR) (trained on WikiMed+PubMedDS+Annotated EHR documents)
- Currently, we provide support with the following entity linkers: cTakes, MetaMap, MetaMapLite, QuickUMLS, and ScispaCy.
- Instructions for runing medtype-as-service follow the instructions given in the readme.md
- Similar to bert-as-service, medtype-as-service is 🔭 State-of-the-art, 🐣 Easy-to-use, ⚡ Fast, 🐙 Scalable, and 💎 Reliable.
- We provide three pre-trained models for tackling different domain:
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medtype-trainer is for training a MedType model from scratch which can be later used by medtype-as-service. All the details for training and evaluation code for entity linking is provided in
./medtype-trainer.
We present two new, automatically-created datasets (available on Google Drive):
- WikiMed: Over 1 million mentions of biomedical concepts in Wikipedia pages
- Mentions were automatically identified based on links to Wikipedia pages for medical concepts.
- Mentions of concepts not linked to Wikipedia pages are not included in the dataset.
- Manual evaluation of 100 random samples found 91% accuracy in the automatic annotations at the level of UMLS concepts, and 95% accuracy in terms of semantic type.
- PubMedDS: Over 57 million mentions of biomedical concepts in abstracts of biomedical research papers on PubMed.
- Mentions were automatically identified using distant supervision, based on and a machine learning NER model in scispaCy.
- Concept identification focused on MeSH headers assigned to the papers.
- Comparison with manually-annotated datasets found 75-90% precision in the automatic annotations.
Datasets statistics:
| Datasets | #Docs | #Sents | #Mentions | #Unq Concepts |
|---|---|---|---|---|
| NCBI | 792 | 7,645 | 6,817 | 1,638 |
| Bio CDR | 1,500 | 14,166 | 28,559 | 9,149 |
| Sharecorpus | 431 | 27,246 | 17,809 | 1,719 |
| MedMentions | 4,392 | 42,602 | 352,496 | 34,724 |
| WikiMed | 393,618 | 11,331,321 | 1,067,083 | 57,739 |
| PubMedDS | 13,197,430 | 127,670,590 | 57,943,354 | 44,881 |
Formatting information:
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Both WikiMed, PubMedDS are in
JSONformat with one document per line. Each document has the following structure:{ "_id": "A unique identifier of each document", "text": "Contains text over which mentions are ", "title": "Title of Wikipedia/PubMed Article", "split": "[Not in PubMedDS] Dataset split: <train/test/valid>", "mentions": [ { "mention": "Surface form of the mention", "start_offset": "Character offset indicating start of the mention", "end_offset": "Character offset indicating end of the mention", "link_id": "UMLS CUI. In case of multiple CUIs, they are concatenated using '|', i.e., CUI1|CUI2|..." }, {} ] } -
We also make two public datasets MedMentions and NCBI Disease corpus also available in the same format. The mapping from Wikipedia to UMLS used for creating the WikiMed dataset has also been made available.
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All the datasets along with the mapping from Wikipedia to UMLS can be downloaded using the following script:
./download_datasets.sh
Please consider citing our paper if you use this code in your work.
@ARTICLE{medtype2020,
author = {{Vashishth}, Shikhar and {Joshi}, Rishabh and {Newman-Griffis}, Denis and
{Dutt}, Ritam and {Rose}, Carolyn},
title = "{MedType: Improving Medical Entity Linking with Semantic Type Prediction}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2020,
month = may,
eid = {arXiv:2005.00460},
pages = {arXiv:2005.00460},
archivePrefix = {arXiv},
eprint = {2005.00460},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv200500460V},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}For any clarification, comments, or suggestions please create an issue or contact Shikhar.
This work was funded in part by NSF grants IIS 1917668 IIS 1822831, Dow Chemical and UPMC Enterprises/Abridge, and the National Library of Medicine of the National Institutes of Health under award number T15 LM007059.