Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kokatnoor, Sujatha Arun; Shukla, Samiksha Title A name given to the resource NLP and Topic Modeling in Healthcare: Identifying Diseases from Patient Histories Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies;Volume;454 SIST;pp.501-516 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-07837-7_38" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-07837-7_38</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036673854?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036673854?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Shukla S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India Description An account of the resource Topic modeling and Natural Language Processing (NLP) have demonstrated significant prospects in the healthcare industry for extracting insightful information from unstructured patient histories that can help diagnose diseases and enhance clinical decisions. In this study, patient histories are grouped into ten different clusters using advanced K-Means clustering, with the Dunn Index being used to validate the clustering performance. After the clusters are formed, each cluster is subjected to topic modeling approaches. Four topic modeling approaches are examined in this study, Latent Dirichlet Allocation (LDA), Hierarchical Dirichlet Process (HDP), Latent Semantic Indexing (LSI), and Non-negative Matrix Factorization (NMF). These techniques are used to find disease-related terms from patient histories. Coherence scores, which show the semantic significance of the terms produced, and execution times, which show the computational efficiency needed for real-time healthcare applications, are used to evaluate the models. According to experimental findings forthe USMLE Step 2 Clinical Skills exam dataset, NMF and HDP generated the most cohesive terms, with NMFs faster execution time (1.67s) making it appropriate for widespread healthcare applications. Whereas, a reasonable balance between coherence and computational demands is offered by LDA and LSI. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Dunn index; K-means clustering; Latent dirichlet allocation (LDA); Latent semantic indexing (LSI); Natural Language Processing (NLP); Non-negative matrix factorization (NMF); Personalized healthcare; Topic modeling Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-303207836-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online