# Multi-Label Learning from Medical Plain Text with Convolutional Residual Models

arXiv: https://arxiv.org/abs/1801.05062 · HTML: https://mlsanity.com/paper/1801.05062/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2018.

## Built on by (most cited first, 6 of 6)

- [Modeling Diagnostic Label Correlation for Automatic ICD Coding](https://mlsanity.com/paper/2106.12800/) (2021)
- [Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation](https://mlsanity.com/paper/2405.05488/) (2024)
- [What Do Patients Say About Their Disease Symptoms? Deep Multilabel Text Classification With Human-in-the-Loop Curation for Automatic Labeling of Patient Self Reports of Problems](https://mlsanity.com/paper/2305.04905/) (2023)
- [DOST -- Domain Obedient Self-supervised Training for Multi Label Classification with Noisy Labels](https://mlsanity.com/paper/2308.05101/) (2023)
- [Analysis and Detection against Network Attacks in the Overlapping Phenomenon of Behavior Attribute](https://mlsanity.com/paper/2310.10660/) (2023)
- [Advancements in eHealth Data Analytics through Natural Language Processing and Deep Learning](https://mlsanity.com/paper/2401.10850/) (2024)

## Abstract (arXiv)

Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required. We employ a Convolutional Neural Network (CNN) to encode plain text into a fixed-length sentence embedding vector. Since diagnoses are typically correlated, a deep residual network is employed on top of the CNN encoder, to capture label (diagnosis) dependencies and incorporate information directly from the encoded sentence vector. A real EHR dataset is considered, and we compare the proposed model with several well-known baselines, to predict diagnoses based on doctor notes. Experimental results demonstrate the superiority of the proposed convolutional residual model.