# Characterizing the Value of Information in Medical Notes

arXiv: https://arxiv.org/abs/2010.03574 · HTML: https://mlsanity.com/paper/2010.03574/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 12 of 12)

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- [Synthetic Data Generation for Augmenting Small Samples](https://mlsanity.com/paper/2501.18741/) (2025)
- [Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text](https://mlsanity.com/paper/2301.10887/) (2023)
- [Enhance the after-discharge mortality rate prediction via learning from the medical notes](https://mlsanity.com/paper/2605.03560/) (2026)
- [A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classification](https://mlsanity.com/paper/2605.14147/) (2026)
- [The record is part of the task: matched-record evaluation of text classifiers across maintenance, safety and recall reporting](https://mlsanity.com/paper/2609.16267/) (2026)

## Abstract (arXiv)

Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints about the quality of medical notes. We use two prediction tasks, readmission prediction and in-hospital mortality prediction, to characterize the value of information in medical notes. We show that as a whole, medical notes only provide additional predictive power over structured information in readmission prediction. We further propose a probing framework to select parts of notes that enable more accurate predictions than using all notes, despite that the selected information leads to a distribution shift from the training data ("all notes"). Finally, we demonstrate that models trained on the selected valuable information achieve even better predictive performance, with only 6.8% of all the tokens for readmission prediction.