Performance metrics for machine learning solutions in laboratory medicine.
Machine learning–based solutions to laboratory medicine problems have become commonplace in literature, but real-world implementations remain rare, in no small part because of the substantial investment required to incorporate such solutions into routine clinical care. A crucial step in advancing a...
| Publicado en: | Laboratory Medicine Vol. 56; no. 6; pp. 597 - 608 |
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| Autores principales: | , |
| Formato: | equations & formulas review tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Nov2025
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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=189866426&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189866426 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00075027 4CY jtl: Laboratory Medicine issn: 00075027 maglogo: N pubinfo: dt: Nov2025 vid: 56 iid: 6 pid: 10398 pub: Oxford University Press / USA artinfo: ui: 189866426 189866426 189866426 10.1093/labmed/lmaf013 189866426 ppf: 597 ppct: 11 formats: tig: atl: Performance metrics for machine learning solutions in laboratory medicine. aug: au: Spies, Nicholas C Ng, David P affil: Department of Pathology, University of Utah, Salt Lake City, UT, United States sug: subj: Machine Learning Clinical Laboratories Problem Solving Medical Practice Sensitivity and Specificity Predictive Value of Tests Diagnosis, Laboratory Diagnosis, Computer Assisted Artificial Intelligence Diagnostic Errors Classification Algorithms ab: Machine learning–based solutions to laboratory medicine problems have become commonplace in literature, but real-world implementations remain rare, in no small part because of the substantial investment required to incorporate such solutions into routine clinical care. A crucial step in advancing a machine learning solution from proof of concept into clinical application is a robust and comprehensive evaluation of its performance. In this review, we discuss the common methods, best practices, and potential pitfalls in evaluating machine learning–based solutions to clinical laboratory problems. pubtype: Academic Journal doctype: equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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