# Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section

arXiv: https://arxiv.org/abs/2307.07051 · HTML: https://mlsanity.com/paper/2307.07051/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences](https://mlsanity.com/paper/2201.11838/) (2022)
- [TAPER: Time-Aware Patient EHR Representation](https://mlsanity.com/paper/1908.03971/) (2019)
- [Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission Prediction](https://mlsanity.com/paper/2211.07047/) (2022)

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

- [Evaluating the performance and fragility of large language models on the self-assessment for neurological surgeons](https://mlsanity.com/paper/2505.23477/) (2025)

## Abstract (arXiv)

Recent advances in large language models have led to renewed interest in natural language processing in healthcare using the free text of clinical notes. One distinguishing characteristic of clinical notes is their long time span over multiple long documents. The unique structure of clinical notes creates a new design choice: when the context length for a language model predictor is limited, which part of clinical notes should we choose as the input? Existing studies either choose the inputs with domain knowledge or simply truncate them. We propose a framework to analyze the sections with high predictive power. Using MIMIC-III, we show that: 1) predictive power distribution is different between nursing notes and discharge notes and 2) combining different types of notes could improve performance when the context length is large. Our findings suggest that a carefully selected sampling function could enable more efficient information extraction from clinical notes.