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

Abstract 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. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The data is available from physionet.org