Revealing the trends in the academic landscape of the health care system using contextual topic modeling

Abstract

The health care system encompasses the participation of individuals, groups, agencies, and resources that offer services to address the requirements of the person, community, and population in terms of health. Parallel to the rising debates on the healthcare systems in relation to diseases, treatments, interventions, medication, and clinical practice guidelines, the world is currently discussing the healthcare industry, technology perspectives, and healthcare costs. To gain a comprehensive understanding of the healthcare systems research paradigm, we offered a novel contextual topic modeling approach that links up the CombinedTM model with our healthcare Bert to discover the contextual topics in the domain of healthcare. This research work discovered 60 contextual topics among them fifteen topics are the hottest which include smart medical monitoring systems, causes, and effects of stress and anxiety, and healthcare cost estimation and twelve topics are the coldest. Moreover, thirty-three topics are showing insignificant trends. We further investigated various clusters and correlations among the topics exploring inter-topic distance maps which add depth to the understanding of the research structure of this scientific domain. The current study enhances the prior topic modeling methodologies that examine the healthcare literature from a particular disciplinary perspective. It further extends the existing topic modeling approaches that do not incorporate contextual information in the topic discovery process adding contextual information by creating sentence embedding vectors through transformers-based models. We also utilized corpus tuning, the mean pooling technique, and the hugging face tool. Our method gives a higher coherence score as compared to the state-of-the-art models (LSA, LDA, and Ber Topic).