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Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine
This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926482/ https://www.ncbi.nlm.nih.gov/pubmed/33671623 http://dx.doi.org/10.3390/diagnostics11020372 |
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author | Ronzio, Luca Cabitza, Federico Barbaro, Alessandro Banfi, Giuseppe |
author_facet | Ronzio, Luca Cabitza, Federico Barbaro, Alessandro Banfi, Giuseppe |
author_sort | Ronzio, Luca |
collection | PubMed |
description | This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies that have applied machine learning techniques to haematochemical parameters and to review their diagnostic and prognostic performance. In doing so, we aim to address the question we asked three years ago about the potential of these techniques in laboratory medicine and the need to leverage a tool that was still under-utilised at that time. |
format | Online Article Text |
id | pubmed-7926482 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79264822021-03-04 Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine Ronzio, Luca Cabitza, Federico Barbaro, Alessandro Banfi, Giuseppe Diagnostics (Basel) Review This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies that have applied machine learning techniques to haematochemical parameters and to review their diagnostic and prognostic performance. In doing so, we aim to address the question we asked three years ago about the potential of these techniques in laboratory medicine and the need to leverage a tool that was still under-utilised at that time. MDPI 2021-02-22 /pmc/articles/PMC7926482/ /pubmed/33671623 http://dx.doi.org/10.3390/diagnostics11020372 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Ronzio, Luca Cabitza, Federico Barbaro, Alessandro Banfi, Giuseppe Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine |
title | Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine |
title_full | Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine |
title_fullStr | Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine |
title_full_unstemmed | Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine |
title_short | Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine |
title_sort | has the flood entered the basement? a systematic literature review about machine learning in laboratory medicine |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926482/ https://www.ncbi.nlm.nih.gov/pubmed/33671623 http://dx.doi.org/10.3390/diagnostics11020372 |
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