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

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.

Machine Learning for Healthcare 2018 spotlight paper

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Cited by in corpus (3)

• SECNLP: A Survey of Embeddings in Clinical Natural Language Processing
• Explainable Prediction of Adverse Outcomes Using Clinical Notes
• Compact Learning for Multi-Label Classification