Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

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

(2017) Stephen F. Weng et al.   PLoS One

Using Electronic Health Records for Population Health Research: A Review of Methods and Applications

(2016) Joan A. Casey et al.   Annual Review of Public Health

On the use of Harrell’s C for clinical risk prediction via random survival forests

(2016) Matthias Schmid et al.   EXPERT SYSTEMS WITH APPLICATIONS

Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

(2016) Benjamin A Goldstein et al.   JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION

The use of machine learning for the identification of peripheral artery disease and future mortality risk

(2016) Elsie Gyang Ross et al.   JOURNAL OF VASCULAR SURGERY

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

(2016) Richard D Riley et al.   BMJ-British Medical Journal

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

(2016) Richard D Riley et al.   BMJ-British Medical Journal

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

(2016) Riccardo Miotto et al.   Scientific Reports

A New Equation to Estimate Glomerular Filtration Rate

(2013) Andrew S. Levey et al.   ANNALS OF INTERNAL MEDICINE

Prognostic models for stable coronary artery disease based on electronic health record cohort of 102 023 patients

(2013) Eleni Rapsomaniki et al.   EUROPEAN HEART JOURNAL

Caveats for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research

(2013) William R. Hersh et al.   MEDICAL CARE

Cox models with dynamic ridge penalties on time-varying effects of the covariates

(2013) Aris Perperoglou   STATISTICS IN MEDICINE

Representativeness and optimal use of body mass index (BMI) in the UK Clinical Practice Research Datalink (CPRD)

(2013) Krishnan Bhaskaran et al.   BMJ Open

Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study

(2013) E. Herrett et al.   BMJ-British Medical Journal

Assessment of performance of survival prediction models for cancer prognosis

(2012) Hung-Chia Chen et al.   BMC Medical Research Methodology

Random forests for genomic data analysis

(2012) Xi Chen et al.   GENOMICS

Data Resource Profile: Cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER)

(2012) S. C. Denaxas et al.   INTERNATIONAL JOURNAL OF EPIDEMIOLOGY

Next-generation phenotyping of electronic health records

(2012) G. Hripcsak et al.   JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION

Comparisons of established risk prediction models for cardiovascular disease: systematic review

(2012) G. C. M. Siontis et al.   BMJ-British Medical Journal

Bias-corrected random forests in regression

(2011) Guoyi Zhang et al.   JOURNAL OF APPLIED STATISTICS

Prediction Modeling Using EHR Data

(2010) Jionglin Wu et al.   MEDICAL CARE

Random survival forests

(2008) Hemant Ishwaran et al.   Annals of Applied Statistics

Conditional Variable Importance for Random Forests

(2008) Carolin Strobl et al.   BMC BIOINFORMATICS

Exploiting missing clinical data in Bayesian network modeling for predicting medical problems

(2007) Jau-Huei Lin et al.   JOURNAL OF BIOMEDICAL INFORMATICS