Exploring diseases based biomedical document clustering and visualization using self-organizing maps

Abstract

Document clustering is a text mining technique used to provide better document search and browsing in digital libraries or online corpora. In this research, a vector representation of concepts of diseases and similarity measurement between concepts are proposed. They identify the closest concepts of diseases in the context of a corpus. Each document is represented by using the vector space model. A weight scheme is proposed to consider both local content and associations between concepts. Self-Organizing Maps (SOM) are often used as document clustering algorithm. The vector projection and visualization features of SOM enable visualization and analysis of the cluster distribution and relationships on the two dimensional space. The Davies-Bouldin index is used to validate the clusters based on the visualized cluster distributions. The results show that the proposed document clustering framework generates meaningful clusters and can facilitate clustering visualization and information retrieval based on the concepts of diseases.

Discovering Key Topics From Short, Real-World Medical Inquiries via Natural Language Processing

“…Examples of applications in the medical domain are clinical event identification in brain cancer patients from clinical reports (Arnold and Speier, 2012), modeling diseases (Pivovarov et al, 2015) and predicting clinical order patterns (Chen et al, 2017) from electronic health records, or detecting cases of noncompliance to drug treatment from patient forums (Abdellaoui et al, 2018). Only recently, word embeddings and unsupervised learning techniques have been combined to analyze unstructured medical text to study the concept of diseases (Shah and Luo, 2017), medical product reviews (Karim et al, 2020), or to extract informative sentences for text summarization (Moradi and Samwald, 2019).…”