ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2019
Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
4 citing papers in PubMed.
- Visualization of medical concepts represented using word embeddings: a scoping review.BMC medical informatics and decision making · 2022Article
- Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?NPJ digital medicine · 2021Article
- Exploiting hierarchy in medical concept embedding.JAMIA open · 2021Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts can be represented in a meaningful, vector space using word embedding models. These embeddings allow for comparison between clinical concepts or for straightforward input to machine learning models. Using traditional approaches, good representations require high dimensionality, making downstream tasks such as visualization more difficult. We applied Poincaré embeddings in a 2-dimensional hyperbolic space to a large-scale administrative claims database and show performance comparable to 100-dimensional embeddings in a euclidean space. We then examine disease relationships under different disease contexts to better understand potential phenotypes.
Indexed as
Identifiers
30864306PMC6417814What OpenQuestion holds
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.