MLD3: Machine Learning for Data-Driven Decisions, University of Michigan
Code repository for work by the MLD3 lab.
Ann Arbor, MI
Pinned Repositories
AMAISE
The source code for AMAISE: A Machine Learning Approach to Index-Free Sequence Enrichment and the accession codes for the data used to train and test AMAISE.
CounterfactualAnnot-SemiOPE
[NeurIPS 2023] Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. https://arxiv.org/abs/2310.17146
Deep-Learning-Applied-to-Chest-X-rays-Exploiting-and-Preventing-Shortcuts
[MLHC 2020] Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts (Jabbour, Fouhey, Kazerooni, Sjoding, Wiens). https://arxiv.org/abs/2009.10132
Deep-Residual-Time-Series-Forecasting
Implementation of architecture for 2020 OhioT1D competition submission. Includes weights from pre-training runs with Tidepool data set. Baseline architecture is N-BEATS, modifications include RNN/shared output blocks, additional Losses. https://folk.idi.ntnu.no/kerstinb/kdh/KDH_ECAI_2020_Proceedings.pdf
FIDDLE
FlexIble Data-Driven pipeLinE – a preprocessing pipeline that transforms structured EHR data into feature vectors to be used with ML algorithms. https://doi.org/10.1093/jamia/ocaa139
FIDDLE-experiments
Experiments applying FIDDLE on MIMIC-III and eICU. https://doi.org/10.1093/jamia/ocaa139
M-CURES
"Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study" (Kamran, Tang, et al.), BMJ 2022.
OfflineRL_FactoredActions
[NeurIPS 2022] Leveraging Factored Action Spaces for Efficient Offline RL in Healthcare.
RL-Set-Valued-Policy
[ICML 2020] Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. https://arxiv.org/abs/2007.12678, https://icml.cc/virtual/2020/poster/5797
RL4BG
Public code release for "Deep Reinforcement Learning for Closed-Loop Blood Glucose Control" (Ian Fox et al.), MLHC 2020. https://arxiv.org/abs/2009.09051
MLD3: Machine Learning for Data-Driven Decisions, University of Michigan's Repositories
MLD3/RL-Set-Valued-Policy
[ICML 2020] Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies. https://arxiv.org/abs/2007.12678, https://icml.cc/virtual/2020/poster/5797
MLD3/RL4BG
Public code release for "Deep Reinforcement Learning for Closed-Loop Blood Glucose Control" (Ian Fox et al.), MLHC 2020. https://arxiv.org/abs/2009.09051
MLD3/Deep-Residual-Time-Series-Forecasting
Implementation of architecture for 2020 OhioT1D competition submission. Includes weights from pre-training runs with Tidepool data set. Baseline architecture is N-BEATS, modifications include RNN/shared output blocks, additional Losses. https://folk.idi.ntnu.no/kerstinb/kdh/KDH_ECAI_2020_Proceedings.pdf
MLD3/complicated_cdi_prediction
Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection. https://doi.org/10.1093/ofid/ofz186
MLD3/hack_aotearoa_intro_ml
Workshop files
MLD3/MLHC2018_SequenceTransformerNetworks
Code release for "Learning to Exploit Invariances in Clinical Time-Series Data Using Sequence Transformer Networks" (Oh, Wang, Wiens), MLHC 2018. https://arxiv.org/abs/1808.06725
MLD3/MLHC2019_Relaxed_Parameter_Sharing
Code release for "Relaxed Weight Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series" (Oh, Wang, Tang, Sjoding, Wiens), MLHC 2019. https://arxiv.org/abs/1906.02898
MLD3/ad_profile_hmm
HMMs to characterize AD. https://doi.org/10.1016/j.dadm.2018.06.007
MLD3/brain_age_prediction
Predicting brain age from MRI data and objectively measured physical activity. https://doi.org/10.1101/525386