{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,13]],"date-time":"2026-11-13T03:00:08Z","timestamp":1794538808125,"version":"4.9.1"},"reference-count":24,"publisher":"BMJ","issue":"7","funder":[{"DOI":"10.13039\/100026028","name":"Association for Computing Machinery\u2019s Special Interest Group on High Performance Computing","doi-asserted-by":"publisher","award":["Computational and Data Science Fellowship"],"award-info":[{"award-number":["Computational and Data Science Fellowship"]}],"id":[{"id":"10.13039\/100026028","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K23-AI150378"],"award-info":[{"award-number":["K23-AI150378"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["T15-LM007079"],"award-info":[{"award-number":["T15-LM007079"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["UM1AI069470"],"award-info":[{"award-number":["UM1AI069470"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Sex Transm Infect"],"accepted":{"date-parts":[[2025,4,13]]},"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aimed to develop robust machine learning (ML)-based and deep learning (DL)-based models capable of detecting mpox cases for surveillance efforts using clinical notes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>As part of a learning health system initiative, we conducted a retrospective study of clinical encounters at the Columbia University Irving Medical Center in New York City. We included patients with mpox diagnoses confirmed by PCR testing between 15 May 2022 and 15 October 2022 and three matched controls for each case based on patient age, sex, race, ethnicity and visit month. We trained three mpox surveillance models using: (1) logistic regression with L1 regularisation (least absolute shrinkage and selection operator (LASSO)), (2) ClinicalBERT and (3) ClinicalLongformer. We evaluated model performance using precision, recall, F1 score, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) and recall at 80% precision (RP80).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The study included 228 PCR-confirmed mpox cases and 698 controls. LASSO regression outperformed the DL models with a precision, recall and F1 score of 0.93, AUROC of 0.97, AUPRC of 0.93 and RP80 of 0.89. ClinicalBERT achieved a precision of 0.88, recall of 0.89, F1 score of 0.88 and AUROC of 0.93. ClinicalLongformer achieved a precision of 0.87, recall of 0.88, F1 score of 0.87 and AUROC of 0.92. Phrases related to symptoms (eg, lesions and pain) were among the most predictive features in LASSO regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML and DL models based on clinical notes show promise for identifying mpox cases. In this study, LASSO regression outperformed DL models and excelled in minimising false positives. These findings highlight the potential for ML and DL methods to support case surveillance for mpox and other infectious diseases. These methods may also prove helpful for flagging missed or delayed diagnoses as part of continuous quality improvement.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/sextrans-2024-056382","type":"journal-article","created":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T03:35:13Z","timestamp":1746243313000},"page":"456-460","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":5,"title":["Development of machine learning-based mpox surveillance models in a learning health system"],"prefix":"10.1136","volume":"101","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7774-2561","authenticated-orcid":false,"given":"Harry","family":"Reyes Nieva","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, USA"},{"name":"Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA"},{"name":"Division of Infectious Diseases, NewYork-Presbyterian Hospital\/Columbia University Irving Medical Center, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6987-6412","authenticated-orcid":false,"given":"Jason","family":"Zucker","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, USA"},{"name":"Division of Infectious Diseases, NewYork-Presbyterian Hospital\/Columbia University Irving Medical Center, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emma","family":"Tucker","sequence":"additional","affiliation":[{"name":"Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"McLean","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, NewYork-Presbyterian Hospital\/Columbia University Irving Medical Center, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clare","family":"DeLaurentis","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, NewYork-Presbyterian Hospital\/Columbia University Irving Medical Center, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shauna","family":"Gunaratne","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, NewYork-Presbyterian Hospital\/Columbia University Irving Medical Center, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"No\u00e9mie","family":"Elhadad","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, USA"},{"name":"Department of Computer Science, Columbia University, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2025,5,2]]},"reference":[{"key":"2025101602151357000_101.7.456.1","unstructured":"NYC department of health and mental hygiene . Mpox 2022 Summary: Cases; 2024. Available: https:\/\/www.nyc.gov\/assets\/doh\/downloads\/pdf\/monkeypox\/mpox-response-data-summary.pdf"},{"key":"2025101602151357000_101.7.456.2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2024.21091"},{"key":"2025101602151357000_101.7.456.3","doi-asserted-by":"publisher","DOI":"10.15585\/mmwr.mm7225a6"},{"key":"2025101602151357000_101.7.456.4","unstructured":"Branswell H . With attention to mpox fading, health officials fear infections will go undetected and unreported. STAT; 2023. Available: https:\/\/www.statnews.com\/2023\/01\/10\/health-officials-fear-mpox-infections-will-go-undetected-unreported\/"},{"key":"2025101602151357000_101.7.456.5","unstructured":"WHO . WHO director-general declares mpox outbreak a public health emergency of international concern. 2024. Available: https:\/\/www.who.int\/news\/item\/14-08-2024-who-director-general-declares-mpox-outbreak-a-public-health-emergency-of-international-concern"},{"key":"2025101602151357000_101.7.456.6","unstructured":"WHO . First meeting of the international health regulations (2005) emergency committee regarding the upsurge of mpox 2024. 2024. Available: https:\/\/www.who.int\/news\/item\/19-08-2024-first-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-upsurge-of-mpox-2024"},{"key":"2025101602151357000_101.7.456.7","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat Biomed Eng"},{"key":"2025101602151357000_101.7.456.8","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-022-01863-7"},{"key":"2025101602151357000_101.7.456.9","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-023-08408-4"},{"key":"2025101602151357000_101.7.456.10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-75030-y"},{"key":"2025101602151357000_101.7.456.11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajic.2024.03.016"},{"key":"2025101602151357000_101.7.456.12","doi-asserted-by":"publisher","DOI":"10.1097\/QAI.0000000000001580"},{"key":"2025101602151357000_101.7.456.13","unstructured":"Institute of Medicine (US) Roundtable on Evidence-Based Medicine . The learning healthcare system: workshop summary. Washington, D.C: National Academies Press, 2007. Available: http:\/\/www.nap.edu\/catalog\/11903"},{"key":"2025101602151357000_101.7.456.14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/505282.505283","article-title":"Machine learning in automated text categorization","volume":"34","author":"Sebastiani","year":"2002","journal-title":"ACM Comput Surv"},{"key":"2025101602151357000_101.7.456.15","unstructured":"Vaswani A , Shazeer N , Parmar N , et al . Attention is all you need. Arxiv; 2023. Available: http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"2025101602151357000_101.7.456.16","unstructured":"Radford A , Narasimhan K , Salimans T , et al . Improving language understanding by generative pre-training."},{"key":"2025101602151357000_101.7.456.17","unstructured":"Devlin J , Chang MW , Lee K , et al . BERT: pre-training of deep bidirectional transformers for language understanding. arXiv; 2019. 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