Building the Model: Challenges and Considerations of Developing and Implementing Machine Learning Tools for Clinical Laboratory Medicine Practice.
* Context.--Machine learning (ML) allows for the analysis of massive quantities of high-dimensional clinical laboratory data, thereby revealing complex patterns and trends. Thus, ML can potentially improve the efficiency of clinical data interpretation and the practice of laboratory medicine. Howeve...
| Published in: | Archives of Pathology & Laboratory Medicine Vol. 147; no. 7; pp. 826 - 837 |
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| Main Authors: | , , , , , |
| Format: | equations & formulas pictorial review tables/charts Journal Article |
| Published: |
College of American Pathologists
Jul2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164738850&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164738850 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Jul2023 vid: 147 iid: 7 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 164738850 164738850 164738850 10.5858/arpa.2021-0635-RA 164738850 ppf: 826 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Building the Model: Challenges and Considerations of Developing and Implementing Machine Learning Tools for Clinical Laboratory Medicine Practice. aug: au: He S. Yang Rhoads, Daniel D. Sepulveda, Jorge Chengxi Zang Chadburn, Amy Fei Wang affil: Departments of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York sug: subj: Machine Learning Clinical Laboratories Medical Practice Data Collection, Computer Assisted Models, Statistical Evaluation Program Implementation Professional Knowledge Workflow Data Management Data Quality ab: * Context.--Machine learning (ML) allows for the analysis of massive quantities of high-dimensional clinical laboratory data, thereby revealing complex patterns and trends. Thus, ML can potentially improve the efficiency of clinical data interpretation and the practice of laboratory medicine. However, the risks of generating biased or unrepresentative models, which can lead to misleading clinical conclusions or overestimation of the model performance, should be recognized. Objectives.--To discuss the major components for creating ML models, including data collection, data preprocessing, model development, and model evaluation. We also highlight many of the challenges and pitfalls in developing ML models, which could result in misleading clinical impressions or inaccurate model performance, and provide suggestions and guidance on how to circumvent these challenges. Data Sources.--The references for this review were identified through searches of the PubMed database, US Food and Drug Administration white papers and guidelines, conference abstracts, and online preprints. Conclusions.--With the growing interest in developing and implementing ML models in clinical practice, laboratorians and clinicians need to be educated in order to collect sufficiently large and high-quality data, properly report the data set characteristics, and combine data from multiple institutions with proper normalization. They will also need to assess the reasons for missing values, determine the inclusion or exclusion of outliers, and evaluate the completeness of a data set. In addition, they require the necessary knowledge to select a suitable ML model for a specific clinical question and accurately evaluate the performance of the ML model, based on objective criteria. Domain-specific knowledge is critical in the entire workflow of developing ML models. pubtype: Academic Journal doctype: equations & formulas pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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