Learning patient similarity using joint distributed embeddings of treatment and diagnoses

TY - JOUR

T1 - Learning patient similarity using joint distributed embeddings of treatment and diagnoses

AU - Ormandy, Christopher

AU - Ibrahim, Zina M.

AU - Dobson, Richard J.B.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85029034905&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85029034905

SN - 1613-0073

VL - 1891

SP - 30

EP - 35

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

ER -