On evaluation metrics for medical applications of artificial intelligence
Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model’s performance. Unfortunately, these...
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| Vydáno v: | Scientific reports Ročník 12; číslo 1; s. 5979 - 9 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Nature Publishing Group UK
08.04.2022
Nature Publishing Group Nature Portfolio |
| Témata: | |
| ISSN: | 2045-2322, 2045-2322 |
| On-line přístup: | Získat plný text |
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| Abstract | Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model’s performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research. |
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| AbstractList | Abstract Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model’s performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research. Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model's performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research. Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model's performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research.Clinicians and software developers need to understand how proposed machine learning (ML) models could improve patient care. No single metric captures all the desirable properties of a model, which is why several metrics are typically reported to summarize a model's performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of binary classification in the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research. |
| ArticleNumber | 5979 |
| Author | Strümke, Inga Thambawita, Vajira Hicks, Steven A. Hammou, Malek Riegler, Michael A. Parasa, Sravanthi Halvorsen, Pål |
| Author_xml | – sequence: 1 givenname: Steven A. surname: Hicks fullname: Hicks, Steven A. email: steven@simula.no organization: SimulaMet, Oslo Metropolitan University – sequence: 2 givenname: Inga surname: Strümke fullname: Strümke, Inga organization: SimulaMet – sequence: 3 givenname: Vajira surname: Thambawita fullname: Thambawita, Vajira organization: SimulaMet, Oslo Metropolitan University – sequence: 4 givenname: Malek surname: Hammou fullname: Hammou, Malek organization: SimulaMet – sequence: 5 givenname: Michael A. surname: Riegler fullname: Riegler, Michael A. organization: SimulaMet – sequence: 6 givenname: Pål surname: Halvorsen fullname: Halvorsen, Pål organization: SimulaMet, Oslo Metropolitan University – sequence: 7 givenname: Sravanthi surname: Parasa fullname: Parasa, Sravanthi organization: Swedish Medical Center |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35395867$$D View this record in MEDLINE/PubMed |
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| Title | On evaluation metrics for medical applications of artificial intelligence |
| URI | https://link.springer.com/article/10.1038/s41598-022-09954-8 https://www.ncbi.nlm.nih.gov/pubmed/35395867 https://www.proquest.com/docview/2648333079 https://www.proquest.com/docview/2648894442 https://pubmed.ncbi.nlm.nih.gov/PMC8993826 https://doaj.org/article/a287165a01b14bddbb770ef788b40403 |
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