BYU

Abstract by Tanner Christensen

Personal Infomation


Presenter's Name

Tanner Christensen

Co-Presenters

David Kartchner

Degree Level

Undergraduate

Co-Authors

David Kartchner
Seth Glazier
Jeffrey Humpherys

Abstract Infomation


Department

Mathematics

Faculty Advisor

Jeffrey Humpherys

Title

Learning Vector-Space Disease Representations from Individual Diagnosis Networks

Abstract

One of the primary challenges of healthcare delivery is aggregating disparate, asynchronous data sources into meaningful indicators of individual health.  We combine natural language word embedding and network modeling techniques to learn meaningful representation of medical concepts by using the weighted network adjacency matrix in the GloVe algorithm.  We demonstrate that using our learned embeddings with basic machine learning techniques performs competitively with state-of-the-art models for disease prediction.