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Reporting of artificial intelligence prediction models

The Lancet (British edition), 2019-04, Vol.393 (10181), p.1577-1579

2019

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Predictive Accuracy of a Polygenic Risk Score-Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease

JAMA : the journal of the American Medical Association, 2020-02, Vol.323 (7), p.636

2020

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Prognosis Research Strategy (PROGRESS) 3: prognostic model research

PLoS medicine, 2013-02, Vol.10 (2), p.e1001381-e1001381

2013

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Prognosis Research Strategy (PROGRESS) 2: prognostic factor research

PLoS medicine, 2013-02, Vol.10 (2), p.e1001380-e1001380

2013

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Reporting and methods in clinical prediction research: a systematic review

PLoS medicine, 2012-05, Vol.9 (5), p.1-12

2012

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Beyond Diagnostic Accuracy: The Clinical Utility of Diagnostic Tests

Clinical chemistry (Baltimore, Md.), 2012-12, Vol.58 (12), p.1636-1643

2012

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Risk prediction models: II. External validation, model updating, and impact assessment

Heart, 2012-05, Vol.98 (9), p.691-698

2012

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Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist

PLoS medicine, 2014-10, Vol.11 (10), p.e1001744-e1001744

2014

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Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis

BMC medicine, 2019-06, Vol.17 (1), p.109-109, Article 109

2019

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No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

BMC medical research methodology, 2016-11, Vol.16 (1), p.163-163, Article 163

2016

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Improvements in risk stratification for the occurrence of cardiovascular disease by imaging subclinical atherosclerosis: a systematic review

Heart, 2012-02, Vol.98 (3), p.177-184

2012

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Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker

Heart, 2012-05, Vol.98 (9), p.683-690

2012

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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement

BMC medicine, 2015-01, Vol.13 (1), p.1-1

2015

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Missing covariate data in medical research: To impute is better than to ignore

Journal of clinical epidemiology, 2010-07, Vol.63 (7), p.721-727

2010

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A new framework to enhance the interpretation of external validation studies of clinical prediction models

Journal of clinical epidemiology, 2015-03, Vol.68 (3), p.279-289

2015

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Individual participant data meta-analysis for a binary outcome: one-stage or two-stage?

PloS one, 2013-04, Vol.8 (4), p.e60650-e60650

2013

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PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Annals of internal medicine, 2019-01, Vol.170 (1), p.51

2019

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Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

NPJ digital medicine, 2022-01, Vol.5 (1), p.2-2, Article 2

2022

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External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges

BMJ (Online), 2016-06, Vol.353, p.i3140

2016

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A guide to systematic review and meta-analysis of prediction model performance

BMJ (Online), 2017-01, Vol.356, p.i6460

2017

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Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement

BMC medicine, 2018-07, Vol.16 (1), p.120-120, Article 120

2018

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

BMC medical research methodology, 2022-01, Vol.22 (1), p.12-12, Article 12

2022

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Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

BMJ (Online), 2020-03, Vol.368, p.l6927

2020

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Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

European heart journal, 2022-08, Vol.43 (31), p.2921-2930

2022

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Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE

Statistics in medicine, 2015-05, Vol.34 (11), p.1841-1863

2015

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Reporting of artificial intelligence prediction models

Predictive Accuracy of a Polygenic Risk Score-Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease

Prognosis Research Strategy (PROGRESS) 3: prognostic model research

Prognosis Research Strategy (PROGRESS) 2: prognostic factor research

Reporting and methods in clinical prediction research: a systematic review

Beyond Diagnostic Accuracy: The Clinical Utility of Diagnostic Tests

Risk prediction models: II. External validation, model updating, and impact assessment

Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist

Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

Improvements in risk stratification for the occurrence of cardiovascular disease by imaging subclinical atherosclerosis: a systematic review

Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement

Missing covariate data in medical research: To impute is better than to ignore

A new framework to enhance the interpretation of external validation studies of clinical prediction models

Individual participant data meta-analysis for a binary outcome: one-stage or two-stage?

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review

External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges

A guide to systematic review and meta-analysis of prediction model performance

Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement

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

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE

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