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Found 52 papers, 27 papers with code
Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations
In this study, we investigate the level of implicit race information available to ML models and human experts and the implications of model-detectable differences in clinical notes.
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Across two different blackbox model architectures and four popular explainability methods, we find that the approximation quality of explanation models, also known as the fidelity, differs significantly between subgroups.
Improving the Fairness of Chest X-ray Classifiers
We also find that methods which achieve group fairness do so by worsening performance for all groups.
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning
Deep metric learning (DML) enables learning with less supervision through its emphasis on the similarity structure of representations.
Semi-Markov Offline Reinforcement Learning for Healthcare
Finally, we apply our new algorithms to a real-world offline dataset pertaining to warfarin dosing for stroke prevention and demonstrate similar results.
Learning Optimal Predictive Checklists
Our results show that our method can fit simple predictive checklists that perform well and that can easily be customized to obey a rich class of custom constraints.
Quantifying the Task-Specific Information in Text-Based Classifications
This framework allows us to compare across datasets, saying that, apart from a set of ``shortcut features'', classifying each sample in the Multi-NLI task involves around 0. 4 nats more TSI than in the Quora Question Pair.
Medical Dead-ends and Learning to Identify High-risk States and Treatments
Machine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal decision-making policy identification via reinforcement learning.
Understanding the Variance Collapse of SVGD in High Dimensions
Stein variational gradient descent (SVGD) is a deterministic inference algorithm that evolves a set of particles to fit a target distribution.
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds
Since naive importance sampling with the marginal density as a proposal requires exponential sample complexity in the true mutual information, we propose novel Multi-Sample Annealed Importance Sampling (AIS) bounds on mutual information.
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing
This systematic investigation underlines the importance of accounting for the underlying data-generating mechanisms and fortifying data-preprocessing pipelines with a causal framework to develop methods robust to confounding biases.
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations
Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinforcing inequities in care access and quality.
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
Finally, we propose few-shot DML as an efficient way to consistently improve generalization in response to unknown test shifts presented in ooDML.
An Empirical Framework for Domain Generalization in Clinical Settings
In this work, we benchmark the performance of eight domain generalization methods on multi-site clinical time series and medical imaging data.
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patients, with a particular focus on offline learning with observational data.
Confounding Feature Acquisition for Causal Effect Estimation
Reliable treatment effect estimation from observational data depends on the availability of all confounding information.
Improving Dialogue Breakdown Detection with Semi-Supervised Learning
In ablations on DBDC4 data from 2019, our semi-supervised learning methods improve the performance of a baseline BERT model by 2\% accuracy.
Multiple Sclerosis Severity Classification From Clinical Text
Next, we present MSBC, a classifier that applies MS-BERT to generate embeddings and predict EDSS and functional subscores.
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings
Our results highlight lesser-known limitations of methods for DP learning in health care, models that exhibit steep tradeoffs between privacy and utility, and models whose predictions are disproportionately influenced by large demographic groups in the training data.
Probabilistic Machine Learning for Healthcare
Machine learning can be used to make sense of healthcare data.
Ethical Machine Learning in Health Care
The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities.
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness
Models that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples.
S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot applications by learning generalizing embedding spaces, although recent work in DML has shown strong performance saturation across training objectives.
Ranked #6 on
Metric Learning
on CARS196
(using extra training data)
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data
Multi-task learning (MTL) is a machine learning technique aiming to improve model performance by leveraging information across many tasks.
Uniform Priors for Data-Efficient Transfer
Deep Neural Networks have shown great promise on a variety of downstream applications; but their ability to adapt and generalize to new data and tasks remains a challenge.
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output
CheXpert is very useful, but is relatively computationally slow, especially when integrated with end-to-end neural pipelines, is non-differentiable so can't be used in any applications that require gradients to flow through the labeler, and does not yield probabilistic outputs, which limits our ability to improve the quality of the silver labeler through techniques such as active learning.
COVID-19 Image Data Collection: Prospective Predictions Are the Future
This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19.
Counterfactually Guided Off-policy Transfer in Clinical Settings
Domain shift, encountered when using a trained model for a new patient population, creates significant challenges for sequential decision making in healthcare since the target domain may be both data-scarce and confounded.
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images.
Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings
In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance on clinical tasks.
CheXclusion: Fairness gaps in deep chest X-ray classifiers
We demonstrate that TPR disparities exist in the state-of-the-art classifiers in all datasets, for all clinical tasks, and all subgroups.
Ranked #1 on
Multi-Label Classification
on ChestX-ray14
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation
We learn mappings from other languages to English and detect aphasia from linguistic characteristics of speech, and show that OT domain adaptation improves aphasia detection over unilingual baselines for French (6% increased F1) and Mandarin (5% increased F1).
Towards Characterizing the High-dimensional Bias of Kernel-based Particle Inference Algorithms
Particle-based inference algorithm is a promising method to efficiently generate samples for an intractable target distribution by iteratively updating a set of particles.
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve.
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced.
Ranked #3 on
Length-of-Stay prediction
on MIMIC-III
Reproducibility in Machine Learning for Health
Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision.
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
Understanding if classifiers generalize to out-of-sample datasets is a central problem in machine learning.
Clinically Accurate Chest X-Ray Report Generation
The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care.
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time.
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech
We analyze the impact of age of the added samples and if they affect fairness in classification.
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.
ClinicalVis: Supporting Clinical Task-Focused Design Evaluation
Making decisions about what clinical tasks to prepare for is multi-factored, and especially challenging in intensive care environments where resources must be balanced with patient needs.
Human-Computer Interaction
Racial Disparities and Mistrust in End-of-Life Care
There are established racial disparities in healthcare, including during end-of-life care, when poor communication and trust can lead to suboptimal outcomes for patients and their families.
Applications
Modeling Mistrust in End-of-Life Care
In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score.
A Review of Challenges and Opportunities in Machine Learning for Health
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions.
Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data
Risk prediction is central to both clinical medicine and public health.
Deep Reinforcement Learning for Sepsis Treatment
Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually.
Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach
In this work, we propose a new approach to deduce optimal treatment policies for septic patients by using continuous state-space models and deep reinforcement learning.
Clinical Intervention Prediction and Understanding using Deep Networks
Real-time prediction of clinical interventions remains a challenge within intensive care units (ICUs).
The Use of Autoencoders for Discovering Patient Phenotypes
We use autoencoders to create low-dimensional embeddings of underlying patient phenotypes that we hypothesize are a governing factor in determining how different patients will react to different interventions.
Uncovering Voice Misuse Using Symbolic Mismatch
Voice disorders affect an estimated 14 million working-aged Americans, and many more worldwide.
