Literature DB >> 29055936

Machine learning in laboratory medicine: waiting for the flood?

Federico Cabitza1,2, Giuseppe Banfi2.   

Abstract

This review focuses on machine learning and on how methods and models combining data analytics and artificial intelligence have been applied to laboratory medicine so far. Although still in its infancy, the potential for applying machine learning to laboratory data for both diagnostic and prognostic purposes deserves more attention by the readership of this journal, as well as by physician-scientists who will want to take advantage of this new computer-based support in pathology and laboratory medicine.

Keywords:  artificial intelligence; diagnostic aids; literature review; machine learning

Mesh:

Year:  2018        PMID: 29055936     DOI: 10.1515/cclm-2017-0287

Source DB:  PubMed          Journal:  Clin Chem Lab Med        ISSN: 1434-6621            Impact factor:   3.694


  13 in total

1.  Temporal and Spatial Dynamics of EEG Features in Female College Students with Subclinical Depression.

Authors:  Shanguang Zhao; Siew-Cheok Ng; Selina Khoo; Aiping Chi
Journal:  Int J Environ Res Public Health       Date:  2022-02-04       Impact factor: 3.390

Review 2.  Applications of machine learning in routine laboratory medicine: Current state and future directions.

Authors:  Naveed Rabbani; Grace Y E Kim; Carlos J Suarez; Jonathan H Chen
Journal:  Clin Biochem       Date:  2022-02-25       Impact factor: 3.281

3.  Oxfordshire Community Stroke Project Classification: A proposed automated algorithm.

Authors:  Joao Brainer Clares de Andrade; Jay P Mohr; Felipe Brito Timbó; Camila Rodrigues Nepomuceno; João Vitor da Silva Moreira; Isabelle da Costa Goes Timbó; Fabricio Oliveira Lima; Gisele Sampaio Silva; John Bamford
Journal:  Eur Stroke J       Date:  2021-06-18

4.  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

5.  Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach.

Authors:  Eyal Klang; Benjamin R Kummer; Neha S Dangayach; Amy Zhong; M Arash Kia; Prem Timsina; Ian Cossentino; Anthony B Costa; Matthew A Levin; Eric K Oermann
Journal:  Sci Rep       Date:  2021-01-14       Impact factor: 4.379

6.  Circular RNA as a Potential Biomarker for Forensic Age Prediction.

Authors:  Junyan Wang; Chunyan Wang; Yangyan Wei; Yanhao Zhao; Can Wang; Chaolong Lu; Jin Feng; Shujin Li; Bin Cong
Journal:  Front Genet       Date:  2022-02-07       Impact factor: 4.599

7.  What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?

Authors:  Vikas Kumar; Christopher Roche; Steven Overman; Ryan Simovitch; Pierre-Henri Flurin; Thomas Wright; Joseph Zuckerman; Howard Routman; Ankur Teredesai
Journal:  Clin Orthop Relat Res       Date:  2020-10       Impact factor: 4.755

Review 8.  Machine Learning in Orthopedics: A Literature Review.

Authors:  Federico Cabitza; Angela Locoro; Giuseppe Banfi
Journal:  Front Bioeng Biotechnol       Date:  2018-06-27

9.  Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests.

Authors:  Song Xu; Jason Hom; Santhosh Balasubramanian; Lee F Schroeder; Nader Najafi; Shivaal Roy; Jonathan H Chen
Journal:  JAMA Netw Open       Date:  2019-09-04

10.  Building more accurate decision trees with the additive tree.

Authors:  José Marcio Luna; Efstathios D Gennatas; Lyle H Ungar; Eric Eaton; Eric S Diffenderfer; Shane T Jensen; Charles B Simone; Jerome H Friedman; Timothy D Solberg; Gilmer Valdes
Journal:  Proc Natl Acad Sci U S A       Date:  2019-09-16       Impact factor: 11.205

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