J
Jie Ma
Researcher at University of Oxford
Publications - 10
Citations - 3425
Jie Ma is an academic researcher from University of Oxford. The author has contributed to research in topics: Systematic review & Critical appraisal. The author has an hindex of 4, co-authored 10 publications receiving 1796 citations. Previous affiliations of Jie Ma include John Radcliffe Hospital.
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Journal ArticleDOI
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
Laure Wynants,Laure Wynants,Ben Van Calster,Ben Van Calster,Gary S. Collins,Gary S. Collins,Richard D Riley,Georg Heinze,Ewoud Schuit,Marc J.M. Bonten,Darren Dahly,Johanna A A G Damen,Thomas P. A. Debray,Valentijn M.T. de Jong,Maarten De Vos,Paula Dhiman,Paula Dhiman,Maria C Haller,Michael O. Harhay,Liesbet Henckaerts,Pauline Heus,Michael Kammer,Nina Kreuzberger,Anna Lohmann,Kim Luijken,Jie Ma,Glen P. Martin,David J. McLernon,Constanza L Andaur Navarro,Johannes B. Reitsma,Jamie C. Sergeant,Chunhu Shi,Nicole Skoetz,Luc J.M. Smits,Kym I E Snell,Matthew Sperrin,René Spijker,René Spijker,Ewout W. Steyerberg,Toshihiko Takada,Ioanna Tzoulaki,Ioanna Tzoulaki,Sander M. J. van Kuijk,Bas C T van Bussel,Bas C T van Bussel,Iwan C. C. van der Horst,Florien S. van Royen,Jan Y Verbakel,Jan Y Verbakel,Christine Wallisch,Christine Wallisch,Jack Wilkinson,Robert Wolff,Lotty Hooft,Karel G.M. Moons,Maarten van Smeden +55 more
TL;DR: Proposed models for covid-19 are poorly reported, at high risk of bias, and their reported performance is probably optimistic, according to a review of published and preprint reports.
Journal ArticleDOI
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
Evangelia Christodoulou,Jie Ma,Gary S. Collins,Ewout W. Steyerberg,Jan Y Verbakel,Ben Van Calster,Ben Van Calster +6 more
TL;DR: Improvements in methodology and reporting are needed for studies that compare modeling algorithms for clinical prediction modeling in the literature and found no evidence of superior performance of ML over LR.
Journal ArticleDOI
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence.
Gary S. Collins,Gary S. Collins,Paula Dhiman,Paula Dhiman,Constanza L Andaur Navarro,Jie Ma,Lotty Hooft,Johannes B. Reitsma,Patricia Logullo,Patricia Logullo,Andrew L. Beam,Lily Peng,Ben Van Calster,Ben Van Calster,Maarten van Smeden,Richard D Riley,Karel G.M. Moons +16 more
TL;DR: TRIPOD-AI as mentioned in this paper is an extension to the TRIPOD statement and the PROBAST (PROBAST-AI) tool to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis.
Journal ArticleDOI
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
Constanza L Andaur Navarro,Johanna A A G Damen,Toshihiko Takada,Steven W J Nijman,Paula Dhiman,Jie Ma,Gary S. Collins,Ram Bajpai,Richard D Riley,Karel G.M. Moons,Lotty Hooft +10 more
TL;DR: In this article, the authors assess the methodological quality of studies on prediction models developed using machine learning techniques across all medical specialties and assess the risk of bias using the prediction risk-of-biased assessment tool (PROBAST).
Journal ArticleDOI
Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.
Constanza L Andaur Navarro,Constanza L Andaur Navarro,Johanna A A G Damen,Johanna A A G Damen,Toshihiko Takada,Steven W J Nijman,Paula Dhiman,Jie Ma,Gary S. Collins,Ram Bajpai,Richard D Riley,Karel G.M. Moons,Karel G.M. Moons,Lotty Hooft,Lotty Hooft +14 more
TL;DR: This comprehensive systematic review aims to evaluate the reporting quality, the methodological conduct, and the risk of bias of prediction model studies that applied ML techniques for model development and/or validation that used AI or ML techniques.