Generalisability through local validation: overcoming barriers due to data disparity in healthcare
Abstract Cho et al. report deep learning model accuracy for tilted myopic disc detection in a South Korean population. Here we explore the importance of generalisability of machine learning (ML) in healthcare, and we emphasise that recurrent underrepresentation of data-poor regions ma...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BioMed Central
2021
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| Online Access: | https://hdl.handle.net/1721.1/136795 |
| _version_ | 1826192497775214592 |
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| author | Mitchell, William G. Dee, Edward C. Celi, Leo A. |
| author_facet | Mitchell, William G. Dee, Edward C. Celi, Leo A. |
| author_sort | Mitchell, William G. |
| collection | MIT |
| description | Abstract
Cho et al. report deep learning model accuracy for tilted myopic disc detection in a South Korean population. Here we explore the importance of generalisability of machine learning (ML) in healthcare, and we emphasise that recurrent underrepresentation of data-poor regions may inadvertently perpetuate global health inequity.
Creating meaningful ML systems is contingent on understanding how, when, and why different ML models work in different settings. While we echo the need for the diversification of ML datasets, such a worthy effort would take time and does not obviate uses of presently available datasets if conclusions are validated and re-calibrated for different groups prior to implementation.
The importance of external ML model validation on diverse populations should be highlighted where possible – especially for models built with single-centre data. |
| first_indexed | 2024-09-23T09:15:43Z |
| format | Article |
| id | mit-1721.1/136795 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:15:43Z |
| publishDate | 2021 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | mit-1721.1/1367952021-11-02T03:39:42Z Generalisability through local validation: overcoming barriers due to data disparity in healthcare Mitchell, William G. Dee, Edward C. Celi, Leo A. Abstract Cho et al. report deep learning model accuracy for tilted myopic disc detection in a South Korean population. Here we explore the importance of generalisability of machine learning (ML) in healthcare, and we emphasise that recurrent underrepresentation of data-poor regions may inadvertently perpetuate global health inequity. Creating meaningful ML systems is contingent on understanding how, when, and why different ML models work in different settings. While we echo the need for the diversification of ML datasets, such a worthy effort would take time and does not obviate uses of presently available datasets if conclusions are validated and re-calibrated for different groups prior to implementation. The importance of external ML model validation on diverse populations should be highlighted where possible – especially for models built with single-centre data. 2021-11-01T14:33:26Z 2021-11-01T14:33:26Z 2021-05-21 2021-05-23T03:16:39Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136795 BMC Ophthalmology. 2021 May 21;21(1):228 PUBLISHER_CC en https://doi.org/10.1186/s12886-021-01992-6 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf BioMed Central BioMed Central |
| spellingShingle | Mitchell, William G. Dee, Edward C. Celi, Leo A. Generalisability through local validation: overcoming barriers due to data disparity in healthcare |
| title | Generalisability through local validation: overcoming barriers due to data disparity in healthcare |
| title_full | Generalisability through local validation: overcoming barriers due to data disparity in healthcare |
| title_fullStr | Generalisability through local validation: overcoming barriers due to data disparity in healthcare |
| title_full_unstemmed | Generalisability through local validation: overcoming barriers due to data disparity in healthcare |
| title_short | Generalisability through local validation: overcoming barriers due to data disparity in healthcare |
| title_sort | generalisability through local validation overcoming barriers due to data disparity in healthcare |
| url | https://hdl.handle.net/1721.1/136795 |
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