Deepr: A Convolutional Net for Medical Records

2016/07/26 by Phuoc Nguyen, Truyen Tran, Nguyen, Phuoc +5 · 2 citations
Computer Science · Health Professions · #Artificial Intelligence in Healthcare #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1607.07519

openalex publication_date 2016/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space.

Citations

• Deep Learning in Neural Networks: An Overview
• Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
• Recurrent Neural Networks for Multivariate Time Series with Missing Values
• Modeling Missing Data in Clinical Time Series with RNNs
• Convolutional Neural Networks for Sentence Classification

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