# Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention

arXiv: https://arxiv.org/abs/2008.01197 · HTML: https://mlsanity.com/paper/2008.01197/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Automated Clinical Coding: What, Why, and Where We Are?](https://mlsanity.com/paper/2203.11092/) (2022)
- [Surpassing GPT-4 Medical Coding with a Two-Stage Approach](https://mlsanity.com/paper/2311.13735/) (2023)
- [Evaluation and LLM-Guided Learning of ICD Coding Rationales](https://mlsanity.com/paper/2508.16777/) (2025)
- [Autoencoder-based prediction of ICU clinical codes](https://mlsanity.com/paper/2305.04992/) (2023)

## Abstract (arXiv)

Problem lists are intended to provide clinicians with a relevant summary of patient medical issues and are embedded in many electronic health record systems. Despite their importance, problem lists are often cluttered with resolved or currently irrelevant conditions. In this work, we develop a novel end-to-end framework that first extracts diagnosis and procedure information from clinical notes and subsequently uses the extracted medical problems to predict patient outcomes. This framework is both more performant and more interpretable than existing models used within the domain, achieving an AU-ROC of 0.710 for bounceback readmission and 0.869 for in-hospital mortality occurring after ICU discharge. We identify risk factors for both readmission and mortality outcomes and demonstrate that our framework can be used to develop dynamic problem lists that present clinical problems along with their quantitative importance. We conduct a qualitative user study with medical experts and demonstrate that they view the lists produced by our framework favorably and find them to be a more effective clinical decision support tool than a strong baseline.