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Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction Model.

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Title: Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction Model.
Authors: Snyder DL; College of Medicine, University of Florida (Drs. Snyder, Chatham, and Tillotson), Gainesville, Florida., Sidhom S; Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida (Dr. Sidhom), Gainesville, Florida., Chatham CE; College of Medicine, University of Florida (Drs. Snyder, Chatham, and Tillotson), Gainesville, Florida., Tillotson SG; College of Medicine, University of Florida (Drs. Snyder, Chatham, and Tillotson), Gainesville, Florida., Zapata RD; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida (Dr. Zapata), Gainesville, Florida., Modave F; Department of Anesthesiology, University of Florida (Dr. Modave), Gainesville, Florida; Department of Pediatrics, Center for Remote Health Monitoring, Center for AI Research, School of Medicine, Wake Forest University (Dr. Modave), Winston-Salem, North Carolina., Solly M; Department of Obstetrics & Gynecology, College of Medicine, University of Florida (Dr. Solly), Gainesville, Florida., Quevedo A; Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics & Gynecology, College of Medicine, University of Florida (Drs. Quevedo and Moawad), Gainesville, Florida., Moawad NS; Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics & Gynecology, College of Medicine, University of Florida (Drs. Quevedo and Moawad), Gainesville, Florida. Electronic address: nmoawad@ufl.edu.
Source: Journal of minimally invasive gynecology [J Minim Invasive Gynecol] 2025 Sep; Vol. 32 (9), pp. 784-792.e12. Date of Electronic Publication: 2025 May 19.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 101235322 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1553-4669 (Electronic) Linking ISSN: 15534650 NLM ISO Abbreviation: J Minim Invasive Gynecol Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction Model.
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  Data: <searchLink fieldCode="AU" term="%22Snyder+DL%22">Snyder DL</searchLink>; College of Medicine, University of Florida (Drs. Snyder, Chatham, and Tillotson), Gainesville, Florida.<br /><searchLink fieldCode="AU" term="%22Sidhom+S%22">Sidhom S</searchLink>; Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida (Dr. Sidhom), Gainesville, Florida.<br /><searchLink fieldCode="AU" term="%22Chatham+CE%22">Chatham CE</searchLink>; College of Medicine, University of Florida (Drs. Snyder, Chatham, and Tillotson), Gainesville, Florida.<br /><searchLink fieldCode="AU" term="%22Tillotson+SG%22">Tillotson SG</searchLink>; College of Medicine, University of Florida (Drs. Snyder, Chatham, and Tillotson), Gainesville, Florida.<br /><searchLink fieldCode="AU" term="%22Zapata+RD%22">Zapata RD</searchLink>; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida (Dr. Zapata), Gainesville, Florida.<br /><searchLink fieldCode="AU" term="%22Modave+F%22">Modave F</searchLink>; Department of Anesthesiology, University of Florida (Dr. Modave), Gainesville, Florida; Department of Pediatrics, Center for Remote Health Monitoring, Center for AI Research, School of Medicine, Wake Forest University (Dr. Modave), Winston-Salem, North Carolina.<br /><searchLink fieldCode="AU" term="%22Solly+M%22">Solly M</searchLink>; Department of Obstetrics & Gynecology, College of Medicine, University of Florida (Dr. Solly), Gainesville, Florida.<br /><searchLink fieldCode="AU" term="%22Quevedo+A%22">Quevedo A</searchLink>; Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics & Gynecology, College of Medicine, University of Florida (Drs. Quevedo and Moawad), Gainesville, Florida.<br /><searchLink fieldCode="AU" term="%22Moawad+NS%22">Moawad NS</searchLink>; Division of Minimally Invasive Gynecologic Surgery, Department of Obstetrics & Gynecology, College of Medicine, University of Florida (Drs. Quevedo and Moawad), Gainesville, Florida. Electronic address: nmoawad@ufl.edu.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier%22">Elsevier </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101235322 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1553-4669 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2215534650%22">15534650 </searchLink><i>NLM ISO Abbreviation: </i>J Minim Invasive Gynecol <i>Subsets: </i>MEDLINE
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              Text: 2025 Sep
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