Clinical Information Extraction via Convolutional Neural Network

2016/03/30 by Peng Li, Heng Huang, Li, Peng +1
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Neural and Evolutionary Computing (cs.NE) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1603.09381

openalex publication_date 2016/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We report an implementation of a clinical information extraction tool that leverages deep neural network to annotate event spans and their attributes from raw clinical notes and pathology reports. Our approach uses context words and their part-of-speech tags and shape information as features. Then we hire temporal (1D) convolutional neural network to learn hidden feature representations. Finally, we use Multilayer Perceptron (MLP) to predict event spans. The empirical evaluation demonstrates that our approach significantly outperforms baselines.

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