Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records. BIBM 2019. Regular Paper Accepted.
Paper link: arXiv:1911.03862
The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative analysis have shown the proposed framework achieves state-of-the-art annotation performance and computational efficiency compared with other methods.
- Python 3.5+
- PyTorch 1.0+
- Others:
- numpy, pandas, tqdm
- etc.
- config.py: hyper-parametres, path configuration
- dataloader.py: path config of data, sources of raw data, preprocessing of data
- Most intermediate data files should be created automatically as long as the raw data is provided. Please submit an issue if not.
- dataset.py: processing of data
- decision.py: annotation strategy
- evaluation.py: evaluation metrics
- loss_func.py: loss functions
- models.py: models
- check Encoder, Generator, PriorConstraintModel
- others are deprecated.
- train.py: controller of train and test
- check UnsupervisedAnnotationController
- others are deprecated.
Jingqing Zhang would like to thank the support from LexisNexis Risk Solutions HPCC Systems academic program. The authors would also like to thank the support from Pangaea Data.