Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning

Trenton Chang • MLHC 2022


Reinforcement Learning with Set-Valued Policies • PathCheck Global Health Innovators Seminar

Shengpu Tang • August 12, 2021


Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings | MLHC 2021

Shengpu Tang • MLHC 2021


Mind the Performance Gap: Examining Dataset Shift During Prospective Validation | MLHC 2021

Erkin Otles and Jeeheh Oh • MLHC 2021


From Diagnosis to Treatment – Augmenting Clinical Decision Making with Artificial Intelligence • Temerty Speaker Series

Dr. Jenna Wiens • May 11, 2021


Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts

Sarah Jabbour • MLHC 2020


Deep Reinforcement Learning for Closed-Loop Blood Glucose Control

Ian Fox • MLHC 2020


Clinician-in-the-loop RL with Set-Valued Policies | ICML 2020

Shengpu Tang | More details on icml.cc


Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction

Presented at 2020 KDH Blood Glucose Level Prediction Challenge


MCURES | CRA Virtual Conference 2020

a COVID predictive model developed specifically for the Michigan Medicine population: the Michigan COVID-19 Utilization and Risk Evaluation System, or MCURES. 


Friday Night AI: AI and COVID-19 | 2020

Erkin Otles speaks about M-CURES, a machine learning model developed by people from our lab. M-CURES can help clinicians tell which COVID-19 patients are most likely to deteriorate.


Stanford Medicine Big Data | Precision Health 2018

Jenna Wiens, University of Michigan


How can machine-learning impact healthcare? | 2018 UM-CSE research highlight

Prof. Jenna Wiens uses machine learning to make sense of the immense amount of patient data generated by modern hospitals. This can help alleviate physician shortages, physician burnout, and the prevalence of medical errors.


AI for Health: Augmenting Clinical Care | Michigan AI Symposium 2018