Corey Chivers

University of Pennsylvania — United States

Medicine · Health Sciences

21h-index2.7kcitations56works13.02yr avg
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Research Topics

Palliative Care and End-of-Life Issues(16), Machine Learning in Healthcare(11), Cancer survivorship and care(6), Sepsis Diagnosis and Treatment(5), Artificial Intelligence in Healthcare and Education(5)

Publications55 total

Intrapartum Electronic Fetal Heart Rate Monitoring to Predict Acidemia at Birth With the Use of Deep Learning
Obstetric Anesthesia Digest·2025· 1 cited
Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning
American Journal of Obstetrics and Gynecology·2024· 25 citedOpen Access
Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis
BMJ Quality & Safety·2023· 7 citedOpen Access
Long-term Effect of Machine Learning–Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer
JAMA Oncology·2023· 95 citedOpen Access
Using Whole Slide Image Representations from Self-supervised Contrastive Learning for Melanoma Concordance Regression
Lecture notes in computer science·2023· 2 cited
Development of Machine Learning Algorithms Incorporating Electronic Health Record Data, Patient-Reported Outcomes, or Both to Predict Mortality for Outpatients With Cancer
JCO Clinical Cancer Informatics·2022· 16 citedOpen Access
Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic
Journal of the American Medical Informatics Association·2022· 13 citedOpen Access
Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression
arXiv (Cornell University)·2022Open Access
Long-term effect of machine learning–triggered behavioral nudges on serious illness communication and end-of-life outcomes among patients with cancer: A randomized clinical trial.
Journal of Clinical Oncology·2022
Oncologist phenotypes and associations with response to a machine learning-based intervention to increase advance care planning: Secondary analysis of a randomized clinical trial
PLoS ONE·2022· 13 citedOpen Access
Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach
Journal of Personalized Medicine·2022· 7 citedOpen Access
Performance Drift in a Mortality Prediction Algorithm during the SARS-CoV-2 Pandemic
2022· 2 citedOpen Access
Two-Stage Approaches to Accounting for Patient Heterogeneity in Machine Learning Risk Prediction Models in Oncology
JCO Clinical Cancer Informatics·2021· 2 citedOpen Access
Behavioral economic implementation strategies to improve serious illness communication between clinicians and high-risk patients with cancer: protocol for a cluster randomized pragmatic trial
Implementation Science·2021· 28 citedOpen Access
Behavioral Economic Implementation Strategies to Improve Serious Illness Communication Between Clinicians and High-risk Patients With Cancer: Protocol for a Cluster Randomized Pragmatic Trial
Research Square (Research Square)·2021Open Access
Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations
PLoS ONE·2021· 32 citedOpen Access
Augmenting machine learning algorithms to predict mortality using patient-reported outcomes in oncology.
Journal of Clinical Oncology·2021
Oncologist phenotypes and associations with response to a behavioral intervention to increase serious illness conversations.
Journal of Clinical Oncology·2021
Patient Interaction Phenotypes With an Automated Remote Hypertension Monitoring Program and Their Association With Blood Pressure Control: Observational Study
Journal of Medical Internet Research·2020· 16 citedOpen Access
Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic
Annals of Internal Medicine·2020· 40 cited
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Frequent Co-authors

Michael Draugelis(19), Ravi B. Parikh(16), Nina O’Connor(12), Lynn M. Schuchter(11), Lawrence N. Shulman(11), Justin E. Bekelman(11), Mitesh S. Patel(10), Brian Leung(7), Christopher R. Manz(7), Susan Harkness Regli(7), Jennifer Braun(7), Jinbo Chen(7), Chalanda N. Evans(6), Barbara E. Engelhardt(6), Amol S. Navathe(5), Dylan S. Small(5), Charles Rareshide(5), Yijie Liu(5), Pallavi Kumar(5), C. William Hanson(4)