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Machine Learning for Data-Driven Decisions (MLD3)
We are a part of the AI Lab within the Division of Computer Science and Engineering. We work at the interface of artificial intelligence (AI), machine learning (ML), and healthcare.
| Welcome to the Machine Learning for Data-Driven Decisions Group at the University of Michigan! 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 wiensj@umich.edu. |
- Two Papers Accepted at CHIL 2023
Congratulations to Harry Rubin-Falcone (Denoising Autoencoders for Learning from Noisy Patient-Reported Data) and Donna Tjandra (Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise) for having their papers accepted at CHIL 2023!
- Honorable Mention at the Kahn Symposium
Congratulations to Meera Krishnamoorthy for winning an honorable mention for her poster at the D. Dan and Betty Kahn Scientific Symposium!
- Paper Accepted at AAAI 2023
Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose was accepted at AAAI 2023. Congratulations to Harry Rubin-Falcone!
- Second Place at the CSE Honors Competition
Congratulations to Shengpu Tang for winning second place at the University of Michigan CSE Honors Competition!
- Two Papers Accepted at NeurIPS 2022
Congratulations to Shengpu Tang (Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare) and Jiaxuan Wang & Sarah Jabbour (Learning Concept Credible Models for Mitigating Shortcuts) for having their papers accepted at NeurIPS 2022!
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning
Trenton Chang • MLHC 2022
PhD student perspectives in CSE: Sarah Jabbour
Sarah Jabbour • 2023
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
Shengpu Tang
Mind the Performance Gap: Examining Dataset Shift During Prospective Validation | MLHC 2021
Erkin Otles
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts | MLHC 2020
Sarah Jabbour
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control | MLHC 2020
Ian Fox
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
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