Literature DB >> 35610537

Statistical methods for validation of predictive models.

Marcio Augusto Diniz1.   

Abstract

Predictive models are widely used in clinical practice. Despite of the increasing number of published AI systems, recent systematic reviews have identified lack of statistical rigor in the development and validation of predictive models. This work reviewed the current literature for predictive performance measures and resampling methods. Furthermore, common pitfalls were discussed.
© 2022. The Author(s) under exclusive licence to American Society of Nuclear Cardiology.

Entities:  

Keywords:  Artificial Intelligence systems; Machine Learning algorithms; Predictive models; Statistical methods

Year:  2022        PMID： 35610537     DOI： 10.1007/s12350-022-02994-7

Source DB:  PubMed          Journal:  J Nucl Cardiol        ISSN： 1071-3581            Impact factor:   5.952


  29 in total

1.  External validity of risk models: Use of benchmark values to disentangle a case-mix effect from incorrect coefficients.

Authors:  Yvonne Vergouwe; Karel G M Moons; Ewout W Steyerberg
Journal:  Am J Epidemiol       Date:  2010-08-31       Impact factor: 4.897

2.  Prediction of coronary heart disease using risk factor categories.

Authors:  P W Wilson; R B D'Agostino; D Levy; A M Belanger; H Silbershatz; W B Kannel
Journal:  Circulation       Date:  1998-05-12       Impact factor: 29.690

3.  A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts.

Authors:  M Malinchoc; P S Kamath; F D Gordon; C J Peine; J Rank; P C ter Borg
Journal:  Hepatology       Date:  2000-04       Impact factor: 17.425

Review 4.  The application of artificial intelligence in nuclear cardiology.

Authors:  Yuka Otaki; Robert J H Miller; Piotr J Slomka
Journal:  Ann Nucl Med       Date:  2022-01-14       Impact factor: 2.668

Review 5.  High-performance medicine: the convergence of human and artificial intelligence.

Authors:  Eric J Topol
Journal:  Nat Med       Date:  2019-01-07       Impact factor: 53.440

6.  Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers.

Authors:  Dong Wook Kim; Hye Young Jang; Kyung Won Kim; Youngbin Shin; Seong Ho Park
Journal:  Korean J Radiol       Date:  2019-03       Impact factor: 3.500

7.  Association of intimate partner violence during pregnancy and birth weight among term births: a cross-sectional study in Kaduna, Northwestern Nigeria.

Authors:  Musa Abubakar Kana; Halima Safiyan; Hauwau Evelyn Yusuf; Abu Saleh Mohammad Musa; Marie Richards-Barber; Quaker E Harmon; Stephanie J London
Journal:  BMJ Open       Date:  2020-12-02       Impact factor: 2.692

8.  Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review.

Authors:  Constanza L Andaur Navarro; Johanna A A 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
Journal:  BMC Med Res Methodol       Date:  2022-01-13       Impact factor: 4.615

9.  Defining CD4 T helper and T regulatory cell endotypes of progressive and remitting pulmonary sarcoidosis (BRITE): protocol for a US-based, multicentre, longitudinal observational bronchoscopy study.

Authors:  Laura L Koth; Laura D Harmacek; Elizabeth K White; Nicholas Kostandinos Arger; Linda Powers; Brenda R Werner; Roman E Magallon; Pineet Grewal; Briana Q Barkes; Li Li; May Gillespie; Sarah E Collins; Jessica Cardenas; Edward S Chen; Lisa A Maier; Sonia M Leach; Brian P O'Connor; Nabeel Y Hamzeh
Journal:  BMJ Open       Date:  2021-11-09       Impact factor: 3.006

10.  Key challenges for delivering clinical impact with artificial intelligence.

Authors:  Christopher J Kelly; Alan Karthikesalingam; Mustafa Suleyman; Greg Corrado; Dominic King
Journal:  BMC Med       Date:  2019-10-29       Impact factor: 8.775
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