Marzyeh Ghassemi

Orcid: 0000-0001-6349-7251

Affiliations:
  • Massachusetts Institute of Technology, USA


According to our database1, Marzyeh Ghassemi authored at least 118 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Using labels to limit AI misuse in health.
Nat. Comput. Sci., September, 2024

Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data.
Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., March, 2024

Large language models in biomedicine and health: current research landscape and future directions.
J. Am. Medical Informatics Assoc., 2024

Identifying Implicit Social Biases in Vision-Language Models.
CoRR, 2024

SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe.
CoRR, 2024

Outlining the Borders for LLM Applications in Patient Education: Developing an Expert-in-the-Loop LLM-Powered Chatbot for Prostate Cancer Patient Education.
CoRR, 2024

LEMoN: Label Error Detection using Multimodal Neighbors.
CoRR, 2024

Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection.
CoRR, 2024

Application-Driven Innovation in Machine Learning.
CoRR, 2024

Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium.
CoRR, 2024

Improving Black-box Robustness with In-Context Rewriting.
CoRR, 2024

Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study.
CoRR, 2024

FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

Asymmetry in Low-Rank Adapters of Foundation Models.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Position: Application-Driven Innovation in Machine Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Measuring Stochastic Data Complexity with Boltzmann Influence Functions.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Can AI Relate: Testing Large Language Model Response for Mental Health Support.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

MisinfoEval: Generative AI in the Era of "Alternative Facts".
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention.
Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024

2023
The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols.
Nat. Mac. Intell., August, 2023

Predicting Out-of-Domain Generalization with Neighborhood Invariance.
Trans. Mach. Learn. Res., 2023

Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning.
Trans. Mach. Learn. Res., 2023

Dissecting the heterogeneity of "in the wild" stress from multimodal sensor data.
npj Digit. Medicine, 2023

Event-Based Contrastive Learning for Medical Time Series.
CoRR, 2023

The Limits of Fair Medical Imaging AI In The Wild.
CoRR, 2023

VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception.
CoRR, 2023

Evaluating the Impact of Social Determinants on Health Prediction.
CoRR, 2023

VisAlign: Dataset for Measuring the Alignment between AI and Humans in Visual Perception.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals.
Proceedings of the Machine Learning for Healthcare Conference, 2023

"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts.
Proceedings of the International Conference on Machine Learning, 2023

Change is Hard: A Closer Look at Subpopulation Shift.
Proceedings of the International Conference on Machine Learning, 2023

When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction.
Proceedings of the International Conference on Machine Learning, 2023

In the Name of Fairness: Assessing the Bias in Clinical Record De-identification.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Taking Off with AI: Lessons from Aviation for Healthcare.
Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, 2023

Clinical Relevance Score for Guided Trauma Injury Pattern Discovery with Weakly Supervised β-VAE.
Proceedings of the Conference on Health, Inference, and Learning, 2023

Foundation Models in Healthcare: Opportunities, Risks & Strategies Forward.
Proceedings of the Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit.
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

2022
In medicine, how do we machine learn anything real?
Patterns, 2022

Better Understanding of the Metamorphosis of Pregnancy (BUMP): protocol for a digital feasibility study in women from preconception to postpartum.
npj Digit. Medicine, 2022

Machine learning and health need better values.
npj Digit. Medicine, 2022

Decision-centered design of a clinical decision support system for acute management of pediatric congenital heart disease.
Frontiers Digit. Health, 2022

Predicting Out-of-Domain Generalization with Local Manifold Smoothness.
CoRR, 2022

When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction.
CoRR, 2022

If Influence Functions are the Answer, Then What is the Question?
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Improving Mutual Information Estimation with Annealed and Energy-Based Bounds.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Understanding the Variance Collapse of SVGD in High Dimensions.
Proceedings of the Tenth International Conference on Learning Representations, 2022

The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Uniform Priors for Data-Efficient Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Improving the Fairness of Chest X-ray Classifiers.
Proceedings of the Conference on Health, Inference, and Learning, 2022

Counterfactually Guided Policy Transfer in Clinical Settings.
Proceedings of the Conference on Health, Inference, and Learning, 2022

Semi-Markov Offline Reinforcement Learning for Healthcare.
Proceedings of the Conference on Health, Inference, and Learning, 2022

Get To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients.
Proceedings of the CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022, 2022

Write It Like You See It: Detectable Differences in Clinical Notes by Race Lead to Differential Model Recommendations.
Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022

2021
Do as AI say: susceptibility in deployment of clinical decision-aids.
npj Digit. Medicine, 2021

Quantifying the Task-Specific Information in Text-Based Classifications.
CoRR, 2021

A comparison of approaches to improve worst-case predictive model performance over patient subpopulations.
CoRR, 2021

Reading Race: AI Recognises Patient's Racial Identity In Medical Images.
CoRR, 2021

CheXclusion: Fairness gaps in deep chest X-ray classifiers.
Proceedings of the Biocomputing 2021: Proceedings of the Pacific Symposium, 2021

Learning Optimal Predictive Checklists.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Medical Dead-ends and Learning to Identify High-Risk States and Treatments.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Simultaneous Similarity-based Self-Distillation for Deep Metric Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing.
Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

An empirical framework for domain generalization in clinical settings.
Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

A comprehensive EHR timeseries pre-training benchmark.
Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

Making Health AI Work in the Real World: Strategies, innovations, and best practices for using AI to improve care delivery.
Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021

2020
Improving Dialogue Breakdown Detection with Semi-Supervised Learning.
CoRR, 2020

Probabilistic Machine Learning for Healthcare.
CoRR, 2020

Ethical Machine Learning in Health Care.
CoRR, 2020

S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning.
CoRR, 2020

A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data.
CoRR, 2020

COVID-19 Image Data Collection: Prospective Predictions Are the Future.
CoRR, 2020

Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning.
CoRR, 2020

CheXclusion: Fairness gaps in deep chest X-ray classifiers.
CoRR, 2020

Confounding Feature Acquisition for Causal Effect Estimation.
Proceedings of the Machine Learning for Health Workshop, 2020

An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare.
Proceedings of the Machine Learning for Health Workshop, 2020

Preparing a Clinical Support Model for Silent Mode in General Internal Medicine.
Proceedings of the Machine Learning for Healthcare Conference, 2020

CheXpert++: Approximating the CheXpert Labeler for Speed, Differentiability, and Probabilistic Output.
Proceedings of the Machine Learning for Healthcare Conference, 2020

SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Hurtful words: quantifying biases in clinical contextual word embeddings.
Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020

MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III.
Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020

Multiple Sclerosis Severity Classification From Clinical Text.
Proceedings of the 3rd Clinical Natural Language Processing Workshop, 2020

2019
Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders.
CoRR, 2019

The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation.
Proceedings of the Machine Learning for Health Workshop, 2019

Learning from Few Subjects with Large Amounts of Voice Monitoring Data.
Proceedings of the Machine Learning for Healthcare Conference, 2019

Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks.
Proceedings of the Machine Learning for Healthcare Conference, 2019

Clinically Accurate Chest X-Ray Report Generation.
Proceedings of the Machine Learning for Healthcare Conference, 2019

Reproducibility in Machine Learning for Health.
Proceedings of the Reproducibility in Machine Learning, 2019

2018
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation.
CoRR, 2018

The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech.
CoRR, 2018

Machine Learning for Health (ML4H) Workshop at NeurIPS 2018.
CoRR, 2018

ClinicalVis: Supporting Clinical Task-Focused Design Evaluation.
CoRR, 2018

Modeling Mistrust in End-of-Life Care.
CoRR, 2018

Opportunities in Machine Learning for Healthcare.
CoRR, 2018

Racial Disparities and Mistrust in End-of-Life Care.
Proceedings of the Machine Learning for Healthcare Conference, 2018

Semi-Supervised Biomedical Translation With Cycle Wasserstein Regression GANs.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Representation learning in multi-dimensional clinical timeseries for risk and event prediction.
PhD thesis, 2017

Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database.
J. Am. Medical Informatics Assoc., 2017

Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data.
CoRR, 2017

Deep Reinforcement Learning for Sepsis Treatment.
CoRR, 2017

The Use of Autoencoders for Discovering Patient Phenotypes.
CoRR, 2017

Clinical Intervention Prediction and Understanding using Deep Networks.
CoRR, 2017

Clinical Intervention Prediction and Understanding with Deep Neural Networks.
Proceedings of the Machine Learning for Health Care Conference, 2017

Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach.
Proceedings of the Machine Learning for Health Care Conference, 2017

Predicting intervention onset in the ICU with switching state space models.
Proceedings of the Summit on Clinical Research Informatics, 2017

2016
Uncovering Voice Misuse Using Symbolic Mismatch.
Proceedings of the 1st Machine Learning in Health Care, 2016

Prediction using patient comparison vs. modeling: A case study for mortality prediction.
Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016

2015
Corrections to "Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results For Vocal Fold Nodules".
IEEE Trans. Biomed. Eng., 2015

A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results for Vocal Fold Nodules.
IEEE Trans. Biomed. Eng., 2014

Unfolding physiological state: mortality modelling in intensive care units.
Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

2013
Probabilistically Populated Medical Record Templates: Reducing Clinical Documentation Time Using Patient Cooperation.
Proceedings of the AMIA 2013, 2013


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