Why the study?
Heart failure carries high rates of unplanned readmissions, and existing models to predict readmissions demonstrate suboptimal performance.
Do embedding-based approaches improve the performance of models predicting 30-day hospital readmission in Heart Failure patients compared to baseline one hot-encoding?
Do embedding-based approaches improve the performance of models predicting 30-day hospital readmission in Heart Failure patients compared to baseline one hot-encoding?
Embedding-based approaches, particularly word2vec, improve the performance of machine learning models for predicting 30-day hospital readmissions in heart failure patients.
Embeddings may enhance HF readmission models; leaves open clinical utility pending prospective validation.
UNSTRUCTURED 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.
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Shakya et al. (2025) studied this question.
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