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

2018/01/15 by Xinyuan Zhang, Ricardo Henao, Zhang, Xinyuan +7 · 1 citation
Computer Science · #Applications (stat.AP) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Natural Language Processing Techniques #Text and Document Classification Technologies #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1801.05062

openalex publication_date 2018/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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