Predicting 30 Days Hospital Readmission for Heart Failure patients using word embeddings

Heart Failure (HF) is a public health concern with a wider impact on quality of life and cost of care. One of the major challenges in HF is the higher rate of unplanned readmissions and sub-optimal performance of models to predict the readmissions. Hence, in this study, we implemented embeddings-based approaches to generate features for improving model performance. Specifically, we compared three embedding approaches including word2vec on terminology codes and CUIs, and BERT on concept descriptions with baseline (one hot-encoding). We found that the embedding approaches significantly improved the performance of the prediction models, and word2vec on the study dataset outperformed pre-trained BERT model.