Changes during dialysis captured in electronic health records help predict near-term risk of sudden cardiac death. - Benjamin Alan Goldstein; Wolfgang C. Winkelmayer; Themistocles L. Assimes (AMIA, 2013)

A Simulation Framework for Longitudinal Electronic Health Records Data. - Matthew Phelan; Benjamin Alan Goldstein (AMIA, 2015)

Classifying individuals based on a densely captured sequence of vital signs: An example using repeated blood pressure measurements during hemodialysis treatment. - Benjamin Alan Goldstein; Tara I. Chang; Wolfgang C. Winkelmayer (J. Biomed. Informatics, 2015)

A Systematic Review of Using Electronic Heath Records to Predict Clinical Events: Assessment of Opportunities and Challenges. - Benjamin Alan Goldstein; Ann Marie Navar; Michael J. Pencina; John P. A. Ioannidis (CRI, 2016)

Informed Presence Bias in the Analysis of Electronic Health Records. - Benjamin Alan Goldstein; Matthew Phelan; Nrupen Bhavsar (CRI, 2017)

Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. - Benjamin Alan Goldstein; Ann Marie Navar; Michael J. Pencina; John P. A. Ioannidis (J. Am. Medical Informatics Assoc., 2017)

Predicting mortality over different time horizons: which data elements are needed? - Benjamin Alan Goldstein; Michael J. Pencina; Maria E. Montez-Rath; Wolfgang C. Winkelmayer (J. Am. Medical Informatics Assoc., 2017)

Assessing electronic health record phenotypes against gold-standard diagnostic criteria for diabetes mellitus. - Susan E. Spratt; Katherine Pereira; Bradi B. Granger; Bryan C. Batch; Matthew Phelan; Michael J. Pencina; Marie Lynn Miranda; L. Ebony Boulware; Joseph E. Lucas; Charlotte L. Nelson; Benjamin Neely; Benjamin Alan Goldstein; et al. (J. Am. Medical Informatics Assoc., 2017)

Lessons Learned from an EHR-Based Population Health Datamart: Southeastern Diabetes Initiative (SEDI). - Ursula A. Rogers; Shelley A. Rusincovitch; Matthew Phelan; Nigel B. Neely; Benjamin Alan Goldstein (AMIA, 2018)

Adversarial Time-to-Event Modeling. - Paidamoyo Chapfuwa; Chenyang Tao; Chunyuan Li; Courtney Page; Benjamin Alan Goldstein; Lawrence Carin; Ricardo Henao (ICML, 2018)

Designing risk prediction models for ambulatory no-shows across different specialties and clinics. - Xiruo Ding; Ziad Gellad; III Chad Mather; Pamela Barth; Eric G. Poon; Mark Newman; Benjamin Alan Goldstein (J. Am. Medical Informatics Assoc., 2018)

An outcome model approach to transporting a randomized controlled trial results to a target population. - Benjamin Alan Goldstein; Matthew Phelan; Neha J. Pagidipati; Rury R. Holman; Michael J. Pencina; Elizabeth A. Stuart (J. Am. Medical Informatics Assoc., 2019)

How and when informative visit processes can bias inference when using electronic health records data for clinical research. - Benjamin Alan Goldstein; Matthew Phelan; Neha J. Pagidipati; Sarah B. Peskoe (J. Am. Medical Informatics Assoc., 2019)

Supercharging Imbalanced Data Learning With Causal Representation Transfer. - Junya Chen; Zidi Xiu; Benjamin Alan Goldstein; Ricardo Henao; Lawrence Carin; Chenyang Tao (CoRR, 2020)

Identified themes of interactive visualizations overlayed onto EHR data: an example of improving birth center operating room efficiency. - Andrew Stirling; Tracy Tubb; Emily S. Reiff; Chad A. Grotegut; Jennifer Gagnon; Weiyi Li; Gail Bradley; Eric G. Poon; Benjamin Alan Goldstein (J. Am. Medical Informatics Assoc., 2020)

Variational Disentanglement for Rare Event Modeling. - Zidi Xiu; Chenyang Tao; Michael Gao; Connor Davis; Benjamin Alan Goldstein; Ricardo Henao (AAAI, 2021)

Comparison of a patient cohort and predictive models derived from local academic medical centers versus a national health database. - Courtney Page; Conrad Sweitek; Cliona Molony; Karen Chandross; Benjamin Alan Goldstein (AMIA, 2021)

Understanding Algorithmic Bias in Clinical Prediction Models. - Mengying Yan; Michael J. Pencina; Benjamin Alan Goldstein (AMIA, 2021)

Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer. - Junya Chen; Zidi Xiu; Benjamin Goldstein 001; Ricardo Henao; Lawrence Carin; Chenyang Tao (NeurIPS, 2021)

Shapley variable importance clouds for interpretable machine learning. - Yilin Ning; Marcus Eng Hock Ong; Bibhas Chakraborty; Benjamin Alan Goldstein; Daniel Shu Wei Ting; Roger Vaughan; Nan Liu 003 (CoRR, 2021)

A framework for the oversight and local deployment of safe and high-quality prediction models. - Armando Bedoya; Nicoleta J. Economou-Zavlanos; Benjamin Alan Goldstein; Allison Young; John Eric Jelovsek; Cara O'Brien; Amanda B. Parrish; Scott Elengold; Kay Lytle; Suresh Balu; Erich Huang; Eric G. Poon; et al. (J. Am. Medical Informatics Assoc., 2022)

Observability and its impact on differential bias for clinical prediction models. - Mengying Yan; Michael J. Pencina; L. Ebony Boulware; Benjamin Alan Goldstein (J. Am. Medical Informatics Assoc., 2022)

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. - Feng Xie 004; Yilin Ning; Han Yuan; Benjamin Alan Goldstein; Marcus Eng Hock Ong; Nan Liu 003; Bibhas Chakraborty (J. Biomed. Informatics, 2022)

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. - Han Yuan; Feng Xie 004; Marcus Eng Hock Ong; Yilin Ning; Marcel Lucas Chee; Seyed Ehsan Saffari; Hairil Rizal Abdullah; Benjamin Alan Goldstein; Bibhas Chakraborty; Nan Liu 003 (J. Biomed. Informatics, 2022)

Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models. - Jillian H. Hurst; Congwen Zhao; Haley P. Hostetler; Mohsen Ghiasi Gorveh; Jason E. Lang; Benjamin Alan Goldstein (BMC Medical Informatics Decis. Mak., 2022)

Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity. - Paul Sabharwal; Jillian H. Hurst; Rohit Tejwani; Kevin T. Hobbs; Jonathan C. Routh; Benjamin Alan Goldstein (BMC Medical Informatics Decis. Mak., 2022)

Correction to: Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity. - Paul Sabharwal; Jillian H. Hurst; Rohit Tejwani; Kevin T. Hobbs; Jonathan C. Routh; Benjamin Alan Goldstein (BMC Medical Informatics Decis. Mak., 2022)

Predicting in-hospital length of stay: a two-stage modeling approach to account for highly skewed data. - Zhenhui Xu; Congwen Zhao; Charles D. Scales; Ricardo Henao; Benjamin Alan Goldstein (BMC Medical Informatics Decis. Mak., 2022)

Shapley variable importance cloud for interpretable machine learning. - Yilin Ning; Marcus Eng Hock Ong; Bibhas Chakraborty; Benjamin Alan Goldstein; Daniel Shu Wei Ting; Roger Vaughan; Nan Liu 003 (Patterns, 2022)

Using natural language processing and structured medical data to phenotype patients hospitalized due to COVID-19. - Feier Chang; Jay Krishnan; Jillian H. Hurst; Michael E. Yarrington; Deverick J. Anderson; Emily C. O'Brien; Benjamin Alan Goldstein (CoRR, 2023)

A roadmap to fair and trustworthy prediction model validation in healthcare. - Yilin Ning; Victor Volovici; Marcus Eng Hock Ong; Benjamin Alan Goldstein; Nan Liu 003 (CoRR, 2023)

Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources. - Meng Xia; Jonathan Wilson; Benjamin Goldstein 001; Ricardo Henao (ICML, 2024)

A conditional multi-label model to improve prediction of a rare outcome: An illustration predicting autism diagnosis. - Wei A. Huang; Matthew Engelhard; Marika Coffman; Elliot D. Hill; Qin Weng; Abby Scheer; Gary Maslow; Ricardo Henao; Geraldine Dawson; Benjamin Alan Goldstein (J. Biomed. Informatics, 2024)

Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare. - Nicoleta J. Economou-Zavlanos; Sophia Bessias; Michael P. Cary; Armando Bedoya; Benjamin Goldstein 001; John Eric Jelovsek; Cara O'Brien; Nancy Walden; Matthew Elmore; Amanda B. Parrish; Scott Elengold; Kay S. Lytle; et al. (J. Am. Medical Informatics Assoc., 2024)

Incorporating informatively collected laboratory data from EHR in clinical prediction models. - Minghui Sun; Matthew M. Engelhard; Armando Bedoya; Benjamin Goldstein 001 (BMC Medical Informatics Decis. Mak., 2024)

Predicting Partially Observed Long-Term Outcomes with Adversarial Positive-Unlabeled Domain Adaptation. - Mengying Yan; Meng Xia; Wei Angel Huang; Chuan Hong; Benjamin Goldstein 001; Matthew M. Engelhard (CHIL, 2025)

Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use. - Jessica Sperling; Whitney Welsh; Erin Haseley; Stella Quenstedt; Perusi B. Muhigaba; Adrian Brown; Patti Ephraim; Tariq Shafi; Michael Waitzkin; David J. Casarett; Benjamin Alan Goldstein (J. Am. Medical Informatics Assoc., 2025)

CLEAR: Unlearning Spurious Style-Content Associations with Contrastive LEarning with Anti-contrastive Regularization. - Minghui Sun; Benjamin Alan Goldstein; Matthew M. Engelhard (CoRR, 2025)

FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport. - Pengxi Liu; Yi Shen 011; Matthew M. Engelhard; Benjamin Alan Goldstein; Michael J. Pencina; Nicoleta J. Economou-Zavlanos; Michael M. Zavlanos (CoRR, 2025)

Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning. - Minghui Sun; Matthew M. Engelhard; Benjamin Alan Goldstein (CoRR, 2025)