# Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning

arXiv: https://arxiv.org/abs/1912.10306 · HTML: https://mlsanity.com/paper/1912.10306/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation](https://mlsanity.com/paper/2104.03969/) (2021)
- [Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions](https://mlsanity.com/paper/2304.09981/) (2023)
- [ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes](https://mlsanity.com/paper/2512.07081/) (2025)
- [Hybrid deep learning methods for phenotype prediction from clinical notes](https://mlsanity.com/paper/2108.10682/) (2021)
- [PT: A Plain Transformer is Good Hospital Readmission Predictor](https://mlsanity.com/paper/2412.12909/) (2024)
- [Interpretable phenotyping of Heart Failure patients with Dutch discharge letters](https://mlsanity.com/paper/2505.24619/) (2025)

## Abstract (arXiv)

Heart failure hospitalization is a severe burden on healthcare. How to predict and therefore prevent readmission has been a significant challenge in outcomes research. To address this, we propose a deep learning approach to predict readmission from clinical notes. Unlike conventional methods that use structured data for prediction, we leverage the unstructured clinical notes to train deep learning models based on convolutional neural networks (CNN). We then use the trained models to classify and predict potentially high-risk admissions/patients. For evaluation, we trained CNNs using the discharge summary notes in the MIMIC III database. We also trained regular machine learning models based on random forest using the same datasets. The result shows that deep learning models outperform the regular models in prediction tasks. CNN method achieves a F1 score of 0.756 in general readmission prediction and 0.733 in 30-day readmission prediction, while random forest only achieves a F1 score of 0.674 and 0.656 respectively. We also propose a chi-square test based method to interpret key features associated with deep learning predicted readmissions. It reveals clinical insights about readmission embedded in the clinical notes. Collectively, our method can make the human evaluation process more efficient and potentially facilitate the reduction of readmission rates.