Use of Topic Analysis for Enhancing Healthcare Technologies

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Usha Patel
Preeti Kathiria
Chand Sahil Mansuri
Shriya Madhvani
Viranchi Parikh


Nowadays, technology has played a vital role in the advancement of the healthcare sector. Various healthcare datasets are available on the web in the form of patents, scientific papers, articles, textual feedback, chatlogs, abstracts of papers, medical reports, and social media posts. It is a tedious task for the stakeholders to find hidden crucial knowledge on the discussed topic from this content, which if utilized optimally can lead to the rapid development of the healthcare sector. Topic analysis concepts are very effective in extracting meaningful topics from the data. Here, frequently applied Topic modeling methods -Latent Semantic Analysis, Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation, Correlated Topics Model, and Non-negative matrix factorization are surveyed along with their benefits and drawbacks. Insights on new innovative topic modeling techniques used in healthcare with their objective, opportunities, and challenges are provided, which can help the researchers for the enhancement of healthcare facilities.

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Special Issue - Scalable Machine Learning for Health Care: Innovations and Applications