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Evaluating the impact of covariate lookback times on performance of patient-level prediction models

BMC medical research methodology, 2021-08, Vol.21 (1), p.1-180, Article 180

2021

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External validation of existing dementia prediction models on observational health data

BMC medical research methodology, 2022-12, Vol.22 (1), p.311-311, Article 311

2022

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Development and validation of a patient-level model to predict dementia across a network of observational databases

BMC medicine, 2024-07, Vol.22 (1), p.308-12, Article 308

2024

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Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation

BMC medical research methodology, 2020-05, Vol.20 (1), p.102-102, Article 102

2020

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Development of multivariable models to predict perinatal depression before and after delivery using patient reported survey responses at weeks 4-10 of pregnancy

BMC pregnancy and childbirth, 2022-05, Vol.22 (1), p.442-442, Article 442

2022

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Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data

Journal of big data, 2024-12, Vol.11 (1), p.7-17, Article 7

2024

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An empirical analysis of dealing with patients who are lost to follow-up when developing prognostic models using a cohort design

BMC medical informatics and decision making, 2021-02, Vol.21 (1), p.43-24, Article 43

2021

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Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study

Translational psychiatry, 2021-12, Vol.11 (1), p.642-642, Article 642

2021

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Design matters in patient-level prediction: evaluation of a cohort vs. case-control design when developing predictive models in observational healthcare datasets

Journal of big data, 2021-08, Vol.8 (1), p.1-18, Article 108

2021

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Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data

Journal of the American Medical Informatics Association : JAMIA, 2018-08, Vol.25 (8), p.969-975

2018

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Machine-learning model to predict the cause of death using a stacking ensemble method for observational data

Journal of the American Medical Informatics Association : JAMIA, 2021-06, Vol.28 (6), p.1098-1107

2021

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Comparing penalization methods for linear models on large observational health data

Journal of the American Medical Informatics Association : JAMIA, 2024-06, Vol.31 (7), p.1514-1521

2024

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Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations

Drug safety, 2022-05, Vol.45 (5), p.493-510

2022

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Investigating the impact of development and internal validation design when training prognostic models using a retrospective cohort in big US observational healthcare data

BMJ open, 2021-12, Vol.11 (12), p.e050146-e050146

2021

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90-Day all-cause mortality can be predicted following a total knee replacement: an international, network study to develop and validate a prediction model

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2022-09, Vol.30 (9), p.3068-3075

2022

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Identifying the DEAD: Development and Validation of a Patient-Level Model to Predict Death Status in Population-Level Claims Data

Drug safety, 2019-11, Vol.42 (11), p.1377-1386

2019

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Using Iterative Pairwise External Validation to Contextualize Prediction Model Performance: A Use Case Predicting 1-Year Heart Failure Risk in Patients with Diabetes Across Five Data Sources

Drug safety, 2022-05, Vol.45 (5), p.563-570

2022

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Developing Predictive Models to Determine Patients in End-of-Life Care in Administrative Datasets

Drug safety, 2020-05, Vol.43 (5), p.447-455

2020

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Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?

International journal of medical informatics (Shannon, Ireland), 2022-07, Vol.163, p.104762-104762, Article 104762

2022

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Supplementing claims data analysis using self-reported data to develop a probabilistic phenotype model for current smoking status

Journal of biomedical informatics, 2019-09, Vol.97, p.103264-103264, Article 103264

2019

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A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

Computer methods and programs in biomedicine, 2021-11, Vol.211, p.106394-106394, Article 106394

2021

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Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining

Computers in biology and medicine, 2016-02, Vol.69, p.61-70

2016

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Does Using a Stacking Ensemble Method to Combine Multiple Base Learners Within a Database Improve Model Transportability?

Studies in health technology and informatics, 2023, Vol.302, p.129

2023

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Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs

Drug safety, 2014-03, Vol.37 (3), p.163-170

2014

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Evaluating the impact of covariate lookback times on performance of patient-level prediction models

External validation of existing dementia prediction models on observational health data

Development and validation of a patient-level model to predict dementia across a network of observational databases

Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation

Development of multivariable models to predict perinatal depression before and after delivery using patient reported survey responses at weeks 4-10 of pregnancy

Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network

Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data

An empirical analysis of dealing with patients who are lost to follow-up when developing prognostic models using a cohort design

Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study

Design matters in patient-level prediction: evaluation of a cohort vs. case-control design when developing predictive models in observational healthcare datasets

Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data

Machine-learning model to predict the cause of death using a stacking ensemble method for observational data

Comparing penalization methods for linear models on large observational health data

Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations

Investigating the impact of development and internal validation design when training prognostic models using a retrospective cohort in big US observational healthcare data

90-Day all-cause mortality can be predicted following a total knee replacement: an international, network study to develop and validate a prediction model

Identifying the DEAD: Development and Validation of a Patient-Level Model to Predict Death Status in Population-Level Claims Data

Using Iterative Pairwise External Validation to Contextualize Prediction Model Performance: A Use Case Predicting 1-Year Heart Failure Risk in Patients with Diabetes Across Five Data Sources

Developing Predictive Models to Determine Patients in End-of-Life Care in Administrative Datasets

Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?

Supplementing claims data analysis using self-reported data to develop a probabilistic phenotype model for current smoking status

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining

Does Using a Stacking Ensemble Method to Combine Multiple Base Learners Within a Database Improve Model Transportability?

Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs

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