Accurate, Robust, and Scalable Machine Abstraction of Mayo Endoscopic Subscores From Colonoscopy Reports.
Background The Mayo endoscopic subscore (MES) is an important quantitative measure of disease activity in ulcerative colitis. Colonoscopy reports in routine clinical care usually characterize ulcerative colitis disease activity using free text description, limiting their utility for clinical researc...
| Publicado en: | Inflammatory Bowel Diseases Vol. 31; no. 3; pp. 665 - 671 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Mar2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184323759&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184323759 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10780998 N0V jtl: Inflammatory Bowel Diseases issn: 10780998 maglogo: N pubinfo: dt: Mar2025 vid: 31 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 184323759 184323759 184323759 10.1093/ibd/izae068 184323759 ppf: 665 ppct: 6 formats: tig: atl: Accurate, Robust, and Scalable Machine Abstraction of Mayo Endoscopic Subscores From Colonoscopy Reports. aug: au: Silverman, Anna L Bhasuran, Balu Mosenia, Arman Yasini, Fatema Ramasamy, Gokul Banerjee, Imon Gupta, Saransh Mardirossian, Taline Narain, Rohan Sewell, Justin Butte, Atul J Rudrapatna, Vivek A affil: Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Phoenix, AZ, USA sug: subj: Colitis, Ulcerative Diagnosis Colitis, Ulcerative Classification Colonoscopy Classification Algorithms Severity of Illness Indices Endoscopy Prediction Models Sensitivity and Specificity Funding Source Human Automation Natural Language Processing Confidence Intervals Logistic Regression P-Value Data Analysis Software Descriptive Statistics ab: Background The Mayo endoscopic subscore (MES) is an important quantitative measure of disease activity in ulcerative colitis. Colonoscopy reports in routine clinical care usually characterize ulcerative colitis disease activity using free text description, limiting their utility for clinical research and quality improvement. We sought to develop algorithms to classify colonoscopy reports according to their MES. Methods We annotated 500 colonoscopy reports from 2 health systems. We trained and evaluated 4 classes of algorithms. Our primary outcome was accuracy in identifying scorable reports (binary) and assigning an MES (ordinal). Secondary outcomes included learning efficiency, generalizability, and fairness. Results Automated machine learning models achieved 98% and 97% accuracy on the binary and ordinal prediction tasks, outperforming other models. Binary models trained on the University of California, San Francisco data alone maintained accuracy (96%) on validation data from Zuckerberg San Francisco General. When using 80% of the training data, models remained accurate for the binary task (97% [n = 320]) but lost accuracy on the ordinal task (67% [n = 194]). We found no evidence of bias by gender (P = .65) or area deprivation index (P = .80). Conclusions We derived a highly accurate pair of models capable of classifying reports by their MES and recognizing when to abstain from prediction. Our models were generalizable on outside institution validation. There was no evidence of algorithmic bias. Our methods have the potential to enable retrospective studies of treatment effectiveness, prospective identification of patients meeting study criteria, and quality improvement efforts in inflammatory bowel diseases. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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