Machine Learning Methods for Disease Prediction with Claims Data

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 representations of medical concepts by using the weighted network adjacency matrix in the GloVe algorithm, which we call Code2Vec. We demonstrate that using our learned embeddings improve neural network performance for disease prediction. However, we also demonstrate that popular deep learning models for disease prediction are not meaningfully better than simpler, more interpretable classifiers such as XGBoost. Additionally, our work adds to the current literature by providing a comprehensive survey of various machine learning algorithms on disease prediction tasks.