A scoping review of artificial intelligence-based methods for diabetes risk prediction
<p dir="ltr">The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2023
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| الموضوعات: | |
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| _version_ | 1864513520757899264 |
|---|---|
| author | Farida Mohsen (16994682) |
| author2 | Hamada R. H. Al-Absi (16726299) Noha A. Yousri (1392577) Nady El Hajj (686554) Zubair Shah (231886) |
| author2_role | author author author author |
| author_facet | Farida Mohsen (16994682) Hamada R. H. Al-Absi (16726299) Noha A. Yousri (1392577) Nady El Hajj (686554) Zubair Shah (231886) |
| author_role | author |
| dc.creator.none.fl_str_mv | Farida Mohsen (16994682) Hamada R. H. Al-Absi (16726299) Noha A. Yousri (1392577) Nady El Hajj (686554) Zubair Shah (231886) |
| dc.date.none.fl_str_mv | 2023-10-25T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41746-023-00933-5 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_scoping_review_of_artificial_intelligence-based_methods_for_diabetes_risk_prediction/26776468 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Public health Type 2 Diabetes Mellitus (T2DM) Predictive Models Early Diagnosis Artificial Intelligence (AI) Machine Learning (ML) Electronic Health Records (EHR) Risk Prediction |
| dc.title.none.fl_str_mv | A scoping review of artificial intelligence-based methods for diabetes risk prediction |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.</p><h2>Other Information</h2><p dir="ltr">Published in: npj Digital Medicine<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41746-023-00933-5" target="_blank">https://dx.doi.org/10.1038/s41746-023-00933-5</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_40f63eea7fe15bd22b632f827bdcbb80 |
| identifier_str_mv | 10.1038/s41746-023-00933-5 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26776468 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A scoping review of artificial intelligence-based methods for diabetes risk predictionFarida Mohsen (16994682)Hamada R. H. Al-Absi (16726299)Noha A. Yousri (1392577)Nady El Hajj (686554)Zubair Shah (231886)Biomedical and clinical sciencesClinical sciencesHealth sciencesHealth services and systemsPublic healthType 2 Diabetes Mellitus (T2DM)Predictive ModelsEarly DiagnosisArtificial Intelligence (AI)Machine Learning (ML)Electronic Health Records (EHR)Risk Prediction<p dir="ltr">The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.</p><h2>Other Information</h2><p dir="ltr">Published in: npj Digital Medicine<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41746-023-00933-5" target="_blank">https://dx.doi.org/10.1038/s41746-023-00933-5</a></p>2023-10-25T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41746-023-00933-5https://figshare.com/articles/journal_contribution/A_scoping_review_of_artificial_intelligence-based_methods_for_diabetes_risk_prediction/26776468CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/267764682023-10-25T09:00:00Z |
| spellingShingle | A scoping review of artificial intelligence-based methods for diabetes risk prediction Farida Mohsen (16994682) Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Public health Type 2 Diabetes Mellitus (T2DM) Predictive Models Early Diagnosis Artificial Intelligence (AI) Machine Learning (ML) Electronic Health Records (EHR) Risk Prediction |
| status_str | publishedVersion |
| title | A scoping review of artificial intelligence-based methods for diabetes risk prediction |
| title_full | A scoping review of artificial intelligence-based methods for diabetes risk prediction |
| title_fullStr | A scoping review of artificial intelligence-based methods for diabetes risk prediction |
| title_full_unstemmed | A scoping review of artificial intelligence-based methods for diabetes risk prediction |
| title_short | A scoping review of artificial intelligence-based methods for diabetes risk prediction |
| title_sort | A scoping review of artificial intelligence-based methods for diabetes risk prediction |
| topic | Biomedical and clinical sciences Clinical sciences Health sciences Health services and systems Public health Type 2 Diabetes Mellitus (T2DM) Predictive Models Early Diagnosis Artificial Intelligence (AI) Machine Learning (ML) Electronic Health Records (EHR) Risk Prediction |