# Deep EHR: Chronic Disease Prediction Using Medical Notes

arXiv: https://arxiv.org/abs/1808.04928 · HTML: https://mlsanity.com/paper/1808.04928/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 15 of 15)

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- [Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System](https://mlsanity.com/paper/2108.10818/) (2021)
- [How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling](https://mlsanity.com/paper/2211.07713/) (2022)
- [An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning](https://mlsanity.com/paper/2211.14539/) (2022)
- [Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers](https://mlsanity.com/paper/2503.03129/) (2025)
- [Time Associated Meta Learning for Clinical Prediction](https://mlsanity.com/paper/2303.02570/) (2023)
- [Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review](https://mlsanity.com/paper/2309.00265/) (2023)
- [Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records](https://mlsanity.com/paper/2507.20993/) (2025)

## Abstract (arXiv)

Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, however, focus on structured fields and lose the vast amount of information inthe unstructured notes. In this work we propose a general multi-task framework for disease onsetprediction that combines both free-text medical notes and structured information. We compareperformance of different deep learning architectures including CNN, LSTM and hierarchical models.In contrast to traditional text-based prediction models, our approach does not require disease specificfeature engineering, and can handle negations and numerical values that exist in the text. Ourresults on a cohort of about 1 million patients show that models using text outperform modelsusing just structured data, and that models capable of using numerical values and negations in thetext, in addition to the raw text, further improve performance. Additionally, we compare differentvisualization methods for medical professionals to interpret model predictions.