Streamlining Quality Review of Mass Spectrometry Data in the Clinical Laboratory by Use of Machine Learning.
* Context.--Turnaround time and productivity of clinical mass spectrometric (MS) testing are hampered by timeconsuming manual review of the analytical quality of MS data before release of patient results. Objective.--To determine whether a classification model created by using standard machine learn...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 143; no. 8; pp. 990 - 999 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Aug2019
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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=137772337&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137772337 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Aug2019 vid: 143 iid: 8 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 137772337 137772337 137772337 10.5858/arpa.2018-0238-OA 137772337 ppf: 990 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Streamlining Quality Review of Mass Spectrometry Data in the Clinical Laboratory by Use of Machine Learning. aug: au: Min Yu Bazydlo, Lindsay A. L. Bruns, David E. Harrison Jr., James H. affil: Division of Laboratory Medicine, Department of Pathology, University of Virginia School of Medicine and Health System, Charlottesville sug: subj: Gas Chromatography-Mass Spectrometry Clinical Laboratories Machine Learning Utilization Algorithms Human Retrospective Design Cannabis Analysis Urinalysis Automation ab: * Context.--Turnaround time and productivity of clinical mass spectrometric (MS) testing are hampered by timeconsuming manual review of the analytical quality of MS data before release of patient results. Objective.--To determine whether a classification model created by using standard machine learning algorithms can verify analytically acceptable MS results and thereby reduce manual review requirements. Design.--We obtained retrospective data from gas chromatography-MS analyses of 11-nor-9-carboxy-delta- 9-tetrahydrocannabinol (THC-COOH) in 1267 urine samples. The data for each sample had been labeled previously as either analytically unacceptable or acceptable by manual review. The dataset was randomly split into training and test sets (848 and 419 samples, respectively), maintaining equal proportions of acceptable (90%) and unacceptable (10%) results in each set. We used stratified 10-fold cross-validation in assessing the abilities of 6 supervised machine learning algorithms to distinguish unacceptable from acceptable assay results in the training dataset. The classifier with the highest recall was used to build a final model, and its performance was evaluated against the test dataset. Results.--In comparison testing of the 6 classifiers, a model based on the Support Vector Machines algorithm yielded the highest recall and acceptable precision. After optimization, this model correctly identified all unacceptable results in the test dataset (100% recall) with a precision of 81%. Conclusions.--Automated data review identified all analytically unacceptable assays in the test dataset, while reducing the manual review requirement by about 87%. This automation strategy can focus manual review only on assays likely to be problematic, allowing improved throughput and turnaround time without reducing quality. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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