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Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PloS one, 2017-04, Vol.12 (4), p.e0174944

2017

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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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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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Wisdom of the CROUD: Development and validation of a patient-level prediction model for opioid use disorder using population-level claims data

PloS one, 2020-02, Vol.15 (2), p.e0228632

2020

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Perinatal depressive symptoms often start in the prenatal rather than postpartum period: results from a longitudinal study

Archives of women's mental health, 2021-02, Vol.24 (1), p.119-131

2021

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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.1-12, Article 308

2024

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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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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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DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models

Nature communications, 2022-03, Vol.13 (1), p.1678-10, Article 1678

2022

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Correction to: Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary

BMC medical informatics and decision making, 2023-06, Vol.23 (1), p.109-109, Article 109

2023

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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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Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases

PloS one, 2019-12, Vol.14 (12), p.e0226255-e0226255

2019

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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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Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary

BMC medical informatics and decision making, 2022-10, Vol.22 (1), p.261-261, Article 261

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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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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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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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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Can machine-learning improve cardiovascular risk prediction using routine clinical data?

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

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

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

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

Wisdom of the CROUD: Development and validation of a patient-level prediction model for opioid use disorder using population-level claims data

Perinatal depressive symptoms often start in the prenatal rather than postpartum period: results from a longitudinal study

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

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

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

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models

Correction to: Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary

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

Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases

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

Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary

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

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

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

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

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