MLD3: Machine Learning for Data-Driven Decisions, University of Michigan

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.

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

OfflineRL_ModelSelection

[MLHC 2021] Model Selection for Offline RL: Practical Considerations for Healthcare Settings. https://arxiv.org/abs/2107.11003

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

OfflineRL_FactoredActions

[NeurIPS 2022] Leveraging Factored Action Spaces for Efficient Offline RL in Healthcare.

ARDS_PLOS_ONE_2019

Machine Learning for Patient Risk Stratification for Acute Respiratory Distress Syndrome (Zeiberg & Prahlad et al.), PLOS ONE, March 2019. https://doi.org/10.1371/journal.pone.0214465

Calibrated-Survival-Analysis

Code Release for "Estimating Calibrated Individualized Survival Curves with Deep Learning" (Kamran & Wiens), AAAI 2021. https://www.aaai.org/AAAI21Papers/AAAI-8472.KamranF.pdf

Combining-chest-X-rays-and-EHR-data-ARF

DTW_physionet2016

Heart Sound Classification based on Temporal Alignment Techniques.

JCO_CCI_aGVHD_prediction

Predicting Acute Graft-versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data from Electronic Health Records (Tang et al.), JCO Clinical Cancer Informatics 2020. https://doi.org/10.1200/CCI.19.00105

sparse-informative-variables

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.

complicated_cdi_prediction

Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection. https://doi.org/10.1093/ofid/ofz186

CounterfactualAnnot-SemiOPE

[NeurIPS 2023] Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. https://arxiv.org/abs/2310.17146

Hierarchical_Survival_Analysis

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

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

AD_from_BP

Predicting Alzheimer's disease onset using blood pressure trajectories

ADTRCI_AD_from_EHR

Code for the paper "Cohort discovery and risk stratification for AD: an EHR-based approach" in Alzheimer's and Dementia: TRCI

AJS_Opioids_Use_Prediction

Predicting Postoperative Opioid Use with Machine Learning and Insurance Claims in Opioid-Naïve Patients (Hur, Tang, ..., Waljee, Wiens). The American Journal of Surgery, 2021. https://doi.org/10.1016/j.amjsurg.2021.03.058

ConceptCredibleModel

credible_learning

[KDD 2018] Learning Credible Models

denoising-autoencoders-for-learning-from-noisy-patient-reported-data

disparate_censorship

ICHE2018_CDIRiskPrediction

Code for ICHE 2018: A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers. https://doi.org/10.1017/ice.2018.16

Instance_Dependent_Label_Noise

Code for "Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise" in CHIL 2023

MILwAPI

Code and Additional Information for "Multiple Instance Learning with Absolute Position Information"

OfflineRL_Pipeline

Optimizing Loop Diuretic Treatment in Hospitalized Patients: A Case Study in Practical Application of Offline Reinforcement Learning to Healthcare