Papers Published

1. Zhang, X; Henao, R; Gan, Z; Li, Y; Carin, L, Multi-Label Learning from Medical Plain Text with Convolutional Residual Models, Proceedings of Machine Learning Research, vol. 85 (January, 2018), pp. 280-294 .
   (last updated on 2024/12/31)

   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.