Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing
© American Association for Clinical Chemistry 2021. All rights reserved. For permissions, please email: journals.permissionsoup.com..
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are poised to transform infectious disease testing. Uniquely, infectious disease testing is technologically diverse spaces in laboratory medicine, where multiple platforms and approaches may be required to support clinical decision-making. Despite advances in laboratory informatics, the vast array of infectious disease data is constrained by human analytical limitations. Machine learning can exploit multiple data streams, including but not limited to laboratory information and overcome human limitations to provide physicians with predictive and actionable results. As a quickly evolving area of computer science, laboratory professionals should become aware of AI/ML applications for infectious disease testing as more platforms are become commercially available.
CONTENT: In this review we: (a) define both AI/ML, (b) provide an overview of common ML approaches used in laboratory medicine, (c) describe the current AI/ML landscape as it relates infectious disease testing, and (d) discuss the future evolution AI/ML for infectious disease testing in both laboratory and point-of-care applications.
SUMMARY: The review provides an important educational overview of AI/ML technique in the context of infectious disease testing. This includes supervised ML approaches, which are frequently used in laboratory medicine applications including infectious diseases, such as COVID-19, sepsis, hepatitis, malaria, meningitis, Lyme disease, and tuberculosis. We also apply the concept of "data fusion" describing the future of laboratory testing where multiple data streams are integrated by AI/ML to provide actionable clinical knowledge.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:68 |
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| Enthalten in: |
Clinical chemistry - 68(2021), 1 vom: 30. Dez., Seite 125-133 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tran, Nam K [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 02.02.2022 Date Revised 18.08.2022 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/clinchem/hvab239 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © American Association for Clinical Chemistry 2021. All rights reserved. For permissions, please email: journals.permissionsoup.com. | ||
| 520 | |a BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are poised to transform infectious disease testing. Uniquely, infectious disease testing is technologically diverse spaces in laboratory medicine, where multiple platforms and approaches may be required to support clinical decision-making. Despite advances in laboratory informatics, the vast array of infectious disease data is constrained by human analytical limitations. Machine learning can exploit multiple data streams, including but not limited to laboratory information and overcome human limitations to provide physicians with predictive and actionable results. As a quickly evolving area of computer science, laboratory professionals should become aware of AI/ML applications for infectious disease testing as more platforms are become commercially available | ||
| 520 | |a CONTENT: In this review we: (a) define both AI/ML, (b) provide an overview of common ML approaches used in laboratory medicine, (c) describe the current AI/ML landscape as it relates infectious disease testing, and (d) discuss the future evolution AI/ML for infectious disease testing in both laboratory and point-of-care applications | ||
| 520 | |a SUMMARY: The review provides an important educational overview of AI/ML technique in the context of infectious disease testing. This includes supervised ML approaches, which are frequently used in laboratory medicine applications including infectious diseases, such as COVID-19, sepsis, hepatitis, malaria, meningitis, Lyme disease, and tuberculosis. We also apply the concept of "data fusion" describing the future of laboratory testing where multiple data streams are integrated by AI/ML to provide actionable clinical knowledge | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Lyme disease | |
| 650 | 4 | |a data stream | |
| 650 | 4 | |a electronic medical records | |
| 650 | 4 | |a immunoassays | |
| 650 | 4 | |a meningitis | |
| 650 | 4 | |a point-of-care testing | |
| 650 | 4 | |a predictive analytics | |
| 650 | 4 | |a sensor fusion | |
| 650 | 4 | |a sepsis | |
| 650 | 4 | |a tuberculosis | |
| 700 | 1 | |a Albahra, Samer |e verfasserin |4 aut | |
| 700 | 1 | |a May, Larissa |e verfasserin |4 aut | |
| 700 | 1 | |a Waldman, Sarah |e verfasserin |4 aut | |
| 700 | 1 | |a Crabtree, Scott |e verfasserin |4 aut | |
| 700 | 1 | |a Bainbridge, Scott |e verfasserin |4 aut | |
| 700 | 1 | |a Rashidi, Hooman |e verfasserin |4 aut | |
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