Multi-Label Learning from Medical Plain Text with Convolutional Residual Models
This work addresses the need for accurate multi-label diagnosis prediction in medical applications, but it appears incremental as it combines existing CNN and residual network techniques.
The paper tackled the problem of predicting multiple diagnoses from doctor notes in Electronic Health Records using a convolutional residual model, achieving superior performance compared to several baselines.
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.