# Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

arXiv: https://arxiv.org/abs/1804.08261 · HTML: https://mlsanity.com/paper/1804.08261/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [A Unified Review of Deep Learning for Automated Medical Coding](https://mlsanity.com/paper/2201.02797/) (2022)
- [NEEDED: Introducing Hierarchical Transformer to Eye Diseases Diagnosis](https://mlsanity.com/paper/2212.13408/) (2022)
- [Deep Learning for Epidemiologists: An Introduction to Neural Networks](https://mlsanity.com/paper/2202.01319/) (2022)
- [Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing](https://mlsanity.com/paper/2205.14761/) (2022)
- [PT: A Plain Transformer is Good Hospital Readmission Predictor](https://mlsanity.com/paper/2412.12909/) (2024)

## Abstract (arXiv)

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.