# Personalized Clinical Note Generation from Doctor-Patient Conversations

arXiv: https://arxiv.org/abs/2408.03874 · HTML: https://mlsanity.com/paper/2408.03874/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 7 Aug 2024.

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

- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation](https://mlsanity.com/paper/2306.02022/) (2023)
- [Investigating Efficiently Extending Transformers for Long Input Summarization](https://mlsanity.com/paper/2208.04347/) (2022)
- [Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations](https://mlsanity.com/paper/2109.12174/) (2021)
- [Revisiting text decomposition methods for NLI-based factuality scoring of summaries](https://mlsanity.com/paper/2211.16853/) (2022)
- [HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models](https://mlsanity.com/paper/2110.04400/) (2021)
- [Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis](https://mlsanity.com/paper/2303.14885/) (2023)
- [Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models](https://mlsanity.com/paper/2305.05982/) (2023)

## Abstract (arXiv)

In this work, we present a novel technique to improve the quality of draft clinical notes for physicians. This technique is concentrated on the ability to model implicit physician conversation styles and note preferences. We also introduce a novel technique for the enrollment of new physicians when a limited number of clinical notes paired with conversations are available for that physician, without the need to re-train a model to support them. We show that our technique outperforms the baseline model by improving the ROUGE-2 score of the History of Present Illness section by 13.8%, the Physical Examination section by 88.6%, and the Assessment & Plan section by 50.8%.