{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T16:57:43Z","timestamp":1791651463393,"version":"4.3.4"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["ebm-journal.org"],"crossmark-restriction":true},"short-container-title":["Exp. Biol. Med."],"abstract":"<jats:p>Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditional topic modeling, such as Latent Dirichlet Allocation (LDA), faces limitations in capturing the semantic relationships in the text document although it has been widely applied in text mining. BERTopic, created in 2022, leveraged advances in deep learning and can capture the contextual relationships between words. In this work, we integrated Artificial Intelligence (AI) modules to LDA and BERTopic and provided a comprehensive comparison on the analysis of prescription opioid-related cardiovascular risks in women. Opioid use can increase the risk of cardiovascular problems in women such as arrhythmia, hypotension etc. 1,837 abstracts were retrieved and downloaded from PubMed as of April 2024 using three Medical Subject Headings (MeSH) words: \u201copioid,\u201d \u201ccardiovascular,\u201d and \u201cwomen.\u201d Machine Learning of Language Toolkit (MALLET) was employed for the implementation of LDA. BioBERT was used for document embedding in BERTopic. Eighteen was selected as the optimal topic number for MALLET and 23 for BERTopic. ChatGPT-4-Turbo was integrated to interpret and compare the results. The short descriptions created by ChatGPT for each topic from LDA and BERTopic were highly correlated, and the performance accuracies of LDA and BERTopic were similar as determined by expert manual reviews of the abstracts grouped by their predominant topics. The results of the t-SNE (t-distributed Stochastic Neighbor Embedding) plots showed that the clusters created from BERTopic were more compact and well-separated, representing improved coherence and distinctiveness between the topics. Our findings indicated that AI algorithms could augment both traditional and contemporary topic modeling techniques. In addition, BERTopic has the connection port for ChatGPT-4-Turbo or other large language models in its algorithm for automatic interpretation, while with LDA interpretation must be manually, and needs special procedures for data pre-processing and stop words exclusion. Therefore, while LDA remains valuable for large-scale text analysis with resource constraints, AI-assisted BERTopic offers significant advantages in providing the enhanced interpretability and the improved semantic coherence for extracting valuable insights from textual data.<\/jats:p>","DOI":"10.3389\/ebm.2025.10389","type":"journal-article","created":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T23:11:27Z","timestamp":1740697887000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":67,"title":["AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women"],"prefix":"10.3389","volume":"250","author":[{"given":"Li","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weigong","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Rogers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beverly","family":"Lyn-Cook","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huixiao","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weida","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningning","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"B1","volume-title":"Understanding the opioid overdose epidemic","year":"2024"},{"key":"B2","volume-title":"Key substance use and mental health indicators in the United States: results from the 2023 national survey on drug use and health","year":"2024"},{"key":"B3","volume-title":"Wide-ranging online data for epidemiologic research (WONDER)","year":"2018"},{"key":"B4","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1097\/mlr.0000000000000625","article-title":"The economic burden of prescription opioid overdose, abuse, and dependence in the United States, 2013","volume":"54","author":"Florence","year":"2016","journal-title":"Med Care"},{"key":"B5","volume-title":"Drug overdose deaths: facts and figures","year":"2019"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1177\/15353702231211860","article-title":"A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database","volume":"248","author":"Le","year":"2023","journal-title":"Exp Biol Med (Maywood)"},{"key":"B7","article-title":"An introduction to topic modeling as an unsupervised machine learning way to organize text information","author":"Snyder","year":"2015","journal-title":"Assoc Supporting Computer Users Education"},{"key":"B8","first-page":"545","volume-title":"Introduction\u2014topic models: what they are and why they matter","author":"Mohr","year":"2013"},{"key":"B9","first-page":"109","article-title":"A study of topic modeling methods","volume-title":"Second international conference on intelligent computing and control systems (ICICCS)","author":"George","year":"2018"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2015.060121","article-title":"A survey of topic modeling in text mining","volume":"6","author":"Alghamdi","year":"2015","journal-title":"Int J Adv Computer Sci Appl"},{"key":"B11","doi-asserted-by":"publisher","first-page":"102131","DOI":"10.1016\/j.is.2022.102131","article-title":"Topic modeling algorithms and applications: a survey","volume":"112","author":"Abdelrazek","year":"2023","journal-title":"Inf Syst"},{"key":"B12","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1561\/1500000030","article-title":"Applications of topic models","volume":"11","author":"Boyd-Graber","year":"2017","journal-title":"Foundations Trends\u00ae Inf Retrieval"},{"key":"B13","doi-asserted-by":"publisher","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","article-title":"Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey","volume":"78","author":"Jelodar","year":"2019","journal-title":"Multimedia tools Appl"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1608","DOI":"10.1186\/s40064-016-3252-8","article-title":"An overview of topic modeling and its current applications in bioinformatics","volume":"5","author":"Liu","year":"2016","journal-title":"SpringerPlus"},{"key":"B15","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1186\/s12859-016-1075-9","article-title":"A novel procedure on next generation sequencing data analysis using text mining algorithm","volume":"17","author":"Zhao","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.2217\/bmm.15.88","article-title":"Biomarker identification from next-generation sequencing data for pathogen bacteria characterization and surveillance","volume":"9","author":"Zhao","year":"2015","journal-title":"Biomark Med"},{"key":"B17","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1186\/s12889-016-2932-1","article-title":"Text mining for identifying topics in the literatures about adolescent substance use and depression","volume":"16","author":"Wang","year":"2016","journal-title":"BMC Public Health"},{"key":"B18","doi-asserted-by":"publisher","first-page":"2741","DOI":"10.1109\/bibm52615.2021.9669550","article-title":"Text fingerprinting and topic mining in the prescription opioid use literature","author":"Le","year":"2021","journal-title":"2021 IEEE Int Conf Bioinformatics Biomed (Bibm)"},{"key":"B19","doi-asserted-by":"publisher","first-page":"101582","DOI":"10.1016\/j.is.2020.101582","article-title":"A review of topic modeling methods","volume":"94","author":"Vayansky","year":"2020","journal-title":"Inf Syst"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.4108\/eai.13-7-2018.159623","article-title":"Topic modeling: a comprehensive review","volume":"7","author":"Kherwa","year":"2019","journal-title":"EAI Endorsed Trans scalable Inf Syst"},{"key":"B21","first-page":"1582","article-title":"Applying topic modeling with prior domain-knowledge in information systems research","volume-title":"Pacific asia conference on information systems","author":"Jiang","year":"2023"},{"key":"B22","article-title":"BERTopic: neural topic modeling with a class-based TF-IDF procedure","author":"Grootendorst","year":"2022"},{"key":"B23","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1145\/1143844.1143967","article-title":"Topic modeling: beyond bag-of-words","volume-title":"Proceedings of the 23rd international conference on Machine learning","author":"Wallach","year":"2006"},{"key":"B24","article-title":"Deriving contextualised semantic features from bert (and other transformer model) embeddings","author":"Turton","year":"2020"},{"key":"B25","volume-title":"The algorithm","author":"Grootendorst","year":"2024"},{"key":"B26","first-page":"867","article-title":"Social media based analysis of opioid epidemic using reddit","volume":"2018","author":"Pandrekar","year":"2018","journal-title":"AMIA Annu Symp Proc"},{"key":"B27","doi-asserted-by":"publisher","first-page":"ooac028","DOI":"10.1093\/jamiaopen\/ooac028","article-title":"Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis","volume":"5","author":"Baird","year":"2022","journal-title":"JAMIA Open"},{"key":"B28","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s44247-023-00029-w","article-title":"A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications","volume":"1","author":"Raza","year":"2023","journal-title":"BMC Digit Health"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2003.07082","article-title":"Stanza: a Python natural language processing toolkit for many human languages","author":"Qi","year":"2020","journal-title":"arXiv preprint arXiv:200307082"},{"key":"B30","volume-title":"MALLET: a machine learning for Language Toolkit","author":"McCallun","year":"2002"},{"key":"B31","doi-asserted-by":"publisher","first-page":"993","DOI":"10.7551\/mitpress\/1120.003.0082","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J machine Learn Res"},{"key":"B32","doi-asserted-by":"publisher","first-page":"S8","DOI":"10.1186\/1471-2105-16-s13-s8","article-title":"A heuristic approach to determine an appropriate number of topics in topic modeling","volume":"16","author":"Zhao","year":"2015","journal-title":"BMC bioinformatics"},{"key":"B33","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.03426","article-title":"Umap: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018","journal-title":"arXiv preprint arXiv:180203426"},{"key":"B35","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/978-3-642-37456-2_14","article-title":"Density-based clustering based on hierarchical density estimates","volume-title":"Pacific-Asia conference on knowledge discovery and data mining","author":"Campello","year":"2013"},{"key":"B36","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.5555\/1953048.2078195","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J machine Learn Res"},{"key":"B37","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J Machine Learn Res"},{"key":"B38","first-page":"262","article-title":"Optimizing semantic coherence in topic models","volume-title":"Proceedings of the 2011 conference on empirical methods in natural language processing2011","author":"Mimno","year":"2024"},{"key":"B39","volume-title":"Software framework for topic modelling with large corpora","author":"\u0158eh\u016f\u0159ek","year":"2010"},{"key":"B40","volume-title":"Data science topics","author":"Coder","year":"2019"},{"key":"B41","volume-title":"Topic model diagnostics","year":"2018"}],"container-title":["Experimental Biology and Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.ebm-journal.org\/articles\/10.3389\/ebm.2025.10389\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T23:11:36Z","timestamp":1740697896000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ebm-journal.org\/articles\/10.3389\/ebm.2025.10389\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,28]]},"references-count":41,"alternative-id":["10.3389\/ebm.2025.10389"],"URL":"https:\/\/doi.org\/10.3389\/ebm.2025.10389","relation":{},"ISSN":["1535-3699"],"issn-type":[{"value":"1535-3699","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,28]]},"article-number":"10389"}}