# ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes

arXiv: https://arxiv.org/abs/2512.07081 · HTML: https://mlsanity.com/paper/2512.07081/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022)
- [Large Language Models to Identify Social Determinants of Health in Electronic Health Records](https://mlsanity.com/paper/2308.06354/) (2023)
- [Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning](https://mlsanity.com/paper/1912.10306/) (2019)
- [Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models](https://mlsanity.com/paper/2504.19061/) (2025)
- [Mining Social Determinants of Health for Heart Failure Patient 30-Day Readmission via Large Language Model](https://mlsanity.com/paper/2502.12158/) (2025)

## Built on by (most cited first, 1 of 1)

- [Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection](https://mlsanity.com/paper/2606.02812/) (2026)

## Abstract (arXiv)

Heart failure (HF) is one of the leading causes of rehospitalization among older adults in the United States. Although clinical notes contain rich, detailed patient information and make up a large portion of electronic health records (EHRs), they remain underutilized for HF readmission risk analysis. Traditional computational models for HF readmission often rely on expert-crafted rules, medical thesauri, and ontologies to interpret clinical notes, which are typically written under time pressure and may contain misspellings, abbreviations, and domain-specific jargon. We present ClinNoteAgents, an LLM-based multi-agent framework that transforms free-text clinical notes into (1) structured representations of clinical and social risk factors for association analysis and (2) clinician-style abstractions for HF 30-day readmission prediction. We evaluate ClinNoteAgents on 3,544 notes from 2,065 patients (readmission rate=35.16%), demonstrating high extraction fidelity for clinical variables (conditional accuracy >= 90% for multiple vitals), key risk factor identification, and preservation of predictive signal despite 60 to 90% text reduction. By reducing reliance on structured fields and minimizing manual annotation and model training, ClinNoteAgents provides a scalable and interpretable approach to note-based HF readmission risk modeling in data-limited healthcare systems.