Name
Title | ||
|---|---|---|
Learning Patient Similarity Using Joint Distributed Embeddings of Treatment and Diagnoses. |
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. |
Year | Venue | Field |
|---|---|---|
2017 | KHD@IJCAI | Embedding,Computer science,Artificial intelligence,Natural language processing,Word embedding,Vocabulary,Machine learning,Medical diagnosis,Formative assessment |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Ormandy | 1 | 0 | 0.34 |
| Zina M. Ibrahim | 2 | 28 | 7.45 |
| Richard Dobson | 3 | 18 | 9.86 |