Ormandy, C;
Ibrahim, ZM;
Dobson, RJB;
(2017)
Learning patient similarity using joint distributed embeddings of treatment and diagnoses.
In: Ibrahim, ZM and Wu, H and Bach, K and Dobson, R and Denaxas, S and Wiratunga, N and Massie, S and Sani, S, (eds.)
KDH@IJCAI 2017: Knowledge Discovery in Healthcare Data: Proceedings of the 2nd International Workshop on Knowledge Discovery in Healthcare Data Co-located with the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017).
(pp. pp. 30-35).
CEUR Workshop Proceedings: Melbourne, Australia.
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Abstract
We propose the use of vector-based word embedding models to learn a cross-conceptual representation of medical vocabulary. The learned model is dense and encodes useful knowledge from the training concepts. Applying the embedding to the concepts of diagnoses and medications, we then show that they can then be used to measure similarities among patient prescriptions, leading to the discovery of in- formative and intuitive relationships between patients.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning patient similarity using joint distributed embeddings of treatment and diagnoses |
| Event: | 2nd International Workshop on Knowledge Discovery in Healthcare Data Co-located with the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017) |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://ceur-ws.org/Vol-1891/paper5.pdf |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10036552 |
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