Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review
Abstract Introduction There was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning‐based prediction model in...
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| Language: | English |
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Wiley
2023-01-01
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| Series: | Acta Obstetricia et Gynecologica Scandinavica |
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| Online Access: | https://doi.org/10.1111/aogs.14475 |
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| author | Qiuyu Yang Xia Fan Xiao Cao Weijie Hao Jiale Lu Jia Wei Jinhui Tian Min Yin Long Ge |
| author_facet | Qiuyu Yang Xia Fan Xiao Cao Weijie Hao Jiale Lu Jia Wei Jinhui Tian Min Yin Long Ge |
| author_sort | Qiuyu Yang |
| collection | DOAJ |
| description | Abstract Introduction There was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning‐based prediction model in preterm birth. Material and methods We conducted a systematic review, searching the PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disk, VIP Database, and WanFang Data from inception to September 27, 2021. Studies that developed (validated) a prediction model using machine learning methods in preterm birth were included. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and Prediction model Risk of Bias Assessment Tool (PROBAST) to evaluate the reporting quality and the risk of bias of included studies, respectively. Findings were summarized using descriptive statistics and visual plots. The protocol was registered in PROSPERO (no. CRD 42022301623). Results Twenty‐nine studies met the inclusion criteria, with 24 development‐only studies and 5 development‐with‐validation studies. Overall, TRIPOD adherence per study ranged from 17% to 79%, with a median adherence of 49%. The reporting of title, abstract, blinding of predictors, sample size justification, explanation of model, and model performance were mostly poor, with TRIPOD adherence ranging from 4% to 17%. For all included studies, 79% had a high overall risk of bias, and 21% had an unclear overall risk of bias. The analysis domain was most commonly rated as high risk of bias in included studies, mainly as a result of small effective sample size, selection of predictors based on univariable analysis, and lack of calibration evaluation. Conclusions Reporting and methodological quality of machine learning‐based prediction models in preterm birth were poor. It is urgent to improve the design, conduct, and reporting of such studies to boost the application of machine learning‐based prediction models in preterm birth in clinical practice. |
| format | Article |
| id | doaj-art-ca68e1ccd433462cb8314b06ade3cc25 |
| institution | Kabale University |
| issn | 0001-6349 1600-0412 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Acta Obstetricia et Gynecologica Scandinavica |
| spelling | doaj-art-ca68e1ccd433462cb8314b06ade3cc252025-08-20T03:30:53ZengWileyActa Obstetricia et Gynecologica Scandinavica0001-63491600-04122023-01-01102171410.1111/aogs.14475Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic reviewQiuyu Yang0Xia Fan1Xiao Cao2Weijie Hao3Jiale Lu4Jia Wei5Jinhui Tian6Min Yin7Long Ge8Evidence‐Based Nursing Center, School of Nursing Lanzhou University Lanzhou ChinaDepartment of Obstetrics and Gynecology, The Second School of Clinical Medicine Shanxi University of Chinese Medicine Shanxi ChinaEvidence‐Based Nursing Center, School of Nursing Lanzhou University Lanzhou ChinaEvidence‐Based Social Science Research Center, School of Public Health Lanzhou University Lanzhou ChinaEvidence‐Based Social Science Research Center, School of Public Health Lanzhou University Lanzhou ChinaEvidence‐Based Social Science Research Center, School of Public Health Lanzhou University Lanzhou ChinaKey Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province Lanzhou ChinaHealth Examination Center The First Hospital of Lanzhou University Lanzhou ChinaEvidence‐Based Social Science Research Center, School of Public Health Lanzhou University Lanzhou ChinaAbstract Introduction There was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning‐based prediction model in preterm birth. Material and methods We conducted a systematic review, searching the PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disk, VIP Database, and WanFang Data from inception to September 27, 2021. Studies that developed (validated) a prediction model using machine learning methods in preterm birth were included. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and Prediction model Risk of Bias Assessment Tool (PROBAST) to evaluate the reporting quality and the risk of bias of included studies, respectively. Findings were summarized using descriptive statistics and visual plots. The protocol was registered in PROSPERO (no. CRD 42022301623). Results Twenty‐nine studies met the inclusion criteria, with 24 development‐only studies and 5 development‐with‐validation studies. Overall, TRIPOD adherence per study ranged from 17% to 79%, with a median adherence of 49%. The reporting of title, abstract, blinding of predictors, sample size justification, explanation of model, and model performance were mostly poor, with TRIPOD adherence ranging from 4% to 17%. For all included studies, 79% had a high overall risk of bias, and 21% had an unclear overall risk of bias. The analysis domain was most commonly rated as high risk of bias in included studies, mainly as a result of small effective sample size, selection of predictors based on univariable analysis, and lack of calibration evaluation. Conclusions Reporting and methodological quality of machine learning‐based prediction models in preterm birth were poor. It is urgent to improve the design, conduct, and reporting of such studies to boost the application of machine learning‐based prediction models in preterm birth in clinical practice.https://doi.org/10.1111/aogs.14475machine learningprediction modelpreterm birthquality of reportingrisk of biassystematic review |
| spellingShingle | Qiuyu Yang Xia Fan Xiao Cao Weijie Hao Jiale Lu Jia Wei Jinhui Tian Min Yin Long Ge Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review Acta Obstetricia et Gynecologica Scandinavica machine learning prediction model preterm birth quality of reporting risk of bias systematic review |
| title | Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review |
| title_full | Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review |
| title_fullStr | Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review |
| title_full_unstemmed | Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review |
| title_short | Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review |
| title_sort | reporting and risk of bias of prediction models based on machine learning methods in preterm birth a systematic review |
| topic | machine learning prediction model preterm birth quality of reporting risk of bias systematic review |
| url | https://doi.org/10.1111/aogs.14475 |
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