Machine Learning for Data-Driven Decisions (MLD3)
Welcome to the Machine Learning for Data-Driven Decisions Group at the University of Michigan!
We work at the interface of artificial intelligence (AI), machine learning (ML), and healthcare.
Our current research portfolio focuses on major public health problems – including infectious disease, Alzheimer’s disease, and diabetes, among others. We develop and apply state-of-the-art AI and machine learning methods to analyze large longitudinal health datasets. Our work spans several aspects of AI including time-series analysis, reinforcement learning, computer vision, and causal inference. We aim to develop the computational methods needed to help organize, process, and transform data into actionable knowledge with the ultimate goal of improving health.
You can contact the group by emailing Dr. Jenna Wiens at email@example.com
Two papers from our lab are accepted at MLHC 2021! Congratulations to students Shengpu Tang (Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings), Erkin Otles, and Jeeheh Oh (Mind the Performance Gap: Examining Dataset Shift During Prospective Validation)!
We released “MIMIC-III and eICU – Feature Representation by FIDDLE Preprocessing” as a dataset on PhysioNet.org!
One paper from our lab, “Shapley Flow: A Graph-Based Approach to Interpreting Model Predictions” is accepted at AISTATS 2021!
Congratulations to student Jiaxuan Wang!
Two papers from our lab are accepted at AAAI 2021! Congratulations to students Donna Tjandra (A Guided Approach to Multi-Event Survival Analysis) and Fahad Kamran (Estimating Calibrated Individualized Survival Curves with Deep Learning)!
From Diagnosis to Treatment – Augmenting Clinical Decision Making with Artificial Intelligence • Temerty Speaker Series
Dr. Jenna Wiens • May 11, 2021
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
Mind the Performance Gap: Examining Dataset Shift During Prospective Validation | MLHC 2021
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts | MLHC 2020
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control | MLHC 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
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.
How can machine learning impact healthcare?
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.
Stanford Medicine Big Data | Precision Health 2018
Dr. Jenna Wiens