mld3 Goto Github PK

ad_from_bp

Predicting Alzheimer's disease onset using blood pressure trajectories

ad_profile_hmm

HMMs to characterize AD. https://doi.org/10.1016/j.dadm.2018.06.007

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

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.

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

brain_age_prediction

Predicting brain age from MRI data and objectively measured physical activity. https://doi.org/10.1101/525386

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

complicated_cdi_prediction

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

conceptcrediblemodel

counterfactualannot-semiope

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

credible_learning

[KDD 2018] Learning Credible Models

dcem

[ICML 2024] From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions

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

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

depict

disparate_censorship

dtw_physionet2016

Heart Sound Classification based on Temporal Alignment Techniques.

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

hack_aotearoa_intro_ml

Workshop files

hierarchical_survival_analysis

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

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

learning-to-rank-for-optimal-treatment-allocation-under-resource-constraints

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.

milwapi

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