Clinical Documents Clustering Based on Medication/Symptom Names Using Multi-View Nonnegative Matrix Factorization
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Updated: Apr 11, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
Abstract:
Clinical documents are rich free-text data sources containing valuable medication and symptom information, which have a great potential to improve health care. In this paper, we build an integrating system for extracting medication names and symptom names from clinical notes. Then we apply nonnegative matrix factorization (NMF) and multi-view NMF to cluster clinical notes into meaningful clusters based on sample-feature matrices. Our experimental results show that multi-view NMF is a preferable method for clinical document clustering. Moreover, we find that using extracted medication/symptom names to cluster clinical documents outperforms just using words.
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