Literature DB >> 35227670

Applications of machine learning in routine laboratory medicine: Current state and future directions.

Naveed Rabbani1, Grace Y E Kim2, Carlos J Suarez3, Jonathan H Chen4.   

Abstract

Machine learning is able to leverage large amounts of data to infer complex patterns that are otherwise beyond the capabilities of rule-based systems and human experts. Its application to laboratory medicine is particularly exciting, as laboratory testing provides much of the foundation for clinical decision making. In this article, we provide a brief introduction to machine learning for the medical professional in addition to a comprehensive literature review outlining the current state of machine learning as it has been applied to routine laboratory medicine. Although still in its early stages, machine learning has been used to automate laboratory tasks, optimize utilization, and provide personalized reference ranges and test interpretation. The published literature leads us to believe that machine learning will be an area of increasing importance for the laboratory practitioner. We envision the laboratory of the future will utilize these methods to make significant improvements in efficiency and diagnostic precision.
Copyright © 2022 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Biochemistry; Clinical decision support; Clinical pathology; Precision medicine

Mesh:

Year:  2022        PMID: 35227670      PMCID: PMC9007900          DOI: 10.1016/j.clinbiochem.2022.02.011

Source DB:  PubMed          Journal:  Clin Biochem        ISSN: 0009-9120            Impact factor:   3.281


  47 in total

1.  Using Machine Learning to Predict Laboratory Test Results.

Authors:  Yuan Luo; Peter Szolovits; Anand S Dighe; Jason M Baron
Journal:  Am J Clin Pathol       Date:  2016-06-21       Impact factor: 2.493

Review 2.  Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.

Authors:  Benjamin A Goldstein; Ann Marie Navar; Michael J Pencina; John P A Ioannidis
Journal:  J Am Med Inform Assoc       Date:  2016-05-17       Impact factor: 4.497

3.  Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.

Authors:  W T Friedewald; R I Levy; D S Fredrickson
Journal:  Clin Chem       Date:  1972-06       Impact factor: 8.327

4.  Big Data and Machine Learning in Health Care.

Authors:  Andrew L Beam; Isaac S Kohane
Journal:  JAMA       Date:  2018-04-03       Impact factor: 56.272

Review 5.  Machine learning in laboratory medicine: waiting for the flood?

Authors:  Federico Cabitza; Giuseppe Banfi
Journal:  Clin Chem Lab Med       Date:  2018-03-28       Impact factor: 3.694

Review 6.  Unsupervised Machine Learning in Pathology: The Next Frontier.

Authors:  Adil Roohi; Kevin Faust; Ugljesa Djuric; Phedias Diamandis
Journal:  Surg Pathol Clin       Date:  2020-03-09

7.  Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.

Authors:  Ziad Obermeyer; Ezekiel J Emanuel
Journal:  N Engl J Med       Date:  2016-09-29       Impact factor: 91.245

8.  The Value of Artificial Intelligence in Laboratory Medicine.

Authors:  Ketan Paranjape; Michiel Schinkel; Richard D Hammer; Bo Schouten; R S Nannan Panday; Paul W G Elbers; Mark H H Kramer; Prabath Nanayakkara
Journal:  Am J Clin Pathol       Date:  2021-05-18       Impact factor: 2.493

9.  Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate.

Authors:  Qingsheng Yang; Kevin M Mwenda; Miao Ge
Journal:  Int J Health Geogr       Date:  2013-03-12       Impact factor: 3.918

10.  Label-free detection of rare circulating tumor cells by image analysis and machine learning.

Authors:  Shen Wang; Yuyuan Zhou; Xiaochen Qin; Suresh Nair; Xiaolei Huang; Yaling Liu
Journal:  Sci Rep       Date:  2020-07-22       Impact factor: 4.379

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  1 in total

Review 1.  Clinlabomics: leveraging clinical laboratory data by data mining strategies.

Authors:  Xiaoxia Wen; Ping Leng; Jiasi Wang; Guishu Yang; Ruiling Zu; Xiaojiong Jia; Kaijiong Zhang; Birga Anteneh Mengesha; Jian Huang; Dongsheng Wang; Huaichao Luo
Journal:  BMC Bioinformatics       Date:  2022-09-24       Impact factor: 3.307

  1 in total

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