ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2017
Joint Learning of Representations of Medical Concepts and Words from EHR Data.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Improving medical term embeddings using UMLS Metathesaurus.BMC medical informatics and decision making · 2022Article
- Article
- Medical Concept Representation Learning from Multi-source Data.IJCAI : proceedings of the conference · 2019Article
- Distributed learning from multiple EHR databases: Contextual embedding models for medical events.Journal of biomedical informatics · 2019Article
- EHR phenotyping via jointly embedding medical concepts and words into a unified vector space.BMC medical informatics and decision making · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
There has been an increasing interest in learning low-dimensional vector representations of medical concepts from electronic health records (EHRs). While EHRs contain structured data such as diagnostic codes and laboratory tests, they also contain unstructured clinical notes, which provide more nuanced details on a patient's health status. In this work, we propose a method that jointly learns medical concept and word representations. In particular, we focus on capturing the relationship between medical codes and words by using a novel learning scheme for word2vec model. Our method exploits relationships between different parts of EHRs in the same visit and embeds both codes and words in the same continuous vector space. In the end, we are able to derive clusters which reflect distinct disease and treatment patterns. In our experiments, we qualitatively show how our methods of grouping words for given diagnostic codes compares with a topic modeling approach. We also test how well our representations can be used to predict disease patterns of the next visit. The results show that our approach outperforms several common methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.