Marzyeh Ghassemi
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Most Influential Person Now
Canada-based researcher in the field of computational medicine
Marzyeh Ghassemi's Degrees
- Bachelors Electrical Engineering and Computer Science University of British Columbia
Why Is Marzyeh Ghassemi Influential?
(Suggest an Edit or Addition)According to Wikipedia, Marzyeh Ghassemi is a Canada-based researcher in the field of computational medicine, where her research focuses on developing machine-learning algorithms to inform health-care decisions. She is currently an assistant professor at the University of Toronto's Department of Computer Science and Faculty of Medicine, and is a Canada CIFAR Artificial Intelligence chair and Canada Research Chair in machine learning for health.
Marzyeh Ghassemi's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- COVID-19 Image Data Collection: Prospective Predictions Are the Future (2020) (541)
- Do no harm: a roadmap for responsible machine learning for health care (2019) (373)
- The false hope of current approaches to explainable artificial intelligence in health care. (2021) (227)
- Unfolding physiological state: mortality modelling in intensive care units (2014) (205)
- A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data (2015) (191)
- Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning (2020) (176)
- Can AI Help Reduce Disparities in General Medical and Mental Health Care? (2019) (156)
- CheXclusion: Fairness gaps in deep chest X-ray classifiers (2020) (149)
- Challenges to the Reproducibility of Machine Learning Models in Health Care. (2020) (147)
- Clinically Accurate Chest X-Ray Report Generation (2019) (146)
- Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach (2017) (141)
- Ethical Machine Learning in Health Care (2020) (140)
- A Review of Challenges and Opportunities in Machine Learning for Health. (2018) (138)
- Deep Reinforcement Learning for Sepsis Treatment (2017) (111)
- Do as AI say: susceptibility in deployment of clinical decision-aids (2021) (110)
- State of the art review: the data revolution in critical care (2015) (99)
- Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations (2021) (98)
- Clinical Intervention Prediction and Understanding using Deep Networks (2017) (95)
- MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III (2019) (92)
- Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update (2015) (85)
- Hurtful words: quantifying biases in clinical contextual word embeddings (2020) (84)
- Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks (2019) (80)
- Opportunities in Machine Learning for Healthcare (2018) (77)
- Reading Race: AI Recognises Patient's Racial Identity In Medical Images (2021) (77)
- Clinical Intervention Prediction and Understanding with Deep Neural Networks (2017) (77)
- Reproducibility in machine learning for health research: Still a ways to go (2021) (75)
- Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference (2014) (67)
- SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness (2020) (66)
- Treating health disparities with artificial intelligence (2020) (59)
- Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results for Vocal Fold Nodules (2014) (58)
- Predicting intervention onset in the ICU with switching state space models (2017) (55)
- Practical guidance on artificial intelligence for health-care data. (2019) (54)
- Characteristics and outcomes of hospital admissions for COVID-19 and influenza in the Toronto area (2021) (50)
- Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data (2015) (50)
- Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database (2017) (47)
- Self-Supervised Contrastive Learning of Protein Representations By Mutual Information Maximization (2020) (46)
- The medical algorithmic audit. (2022) (44)
- The role of machine learning in clinical research: transforming the future of evidence generation (2021) (41)
- Equity in essence: a call for operationalising fairness in machine learning for healthcare (2021) (39)
- What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask (2021) (38)
- Implementing machine learning in medicine (2021) (37)
- An empirical framework for domain generalization in clinical settings (2021) (36)
- Reproducibility in Machine Learning for Health (2019) (36)
- Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings (2020) (35)
- Leveraging a critical care database: selective serotonin reuptake inhibitor use prior to ICU admission is associated with increased hospital mortality. (2014) (34)
- AI recognition of patient race in medical imaging: a modelling study (2022) (33)
- Ambulatory assessment of phonotraumatic vocal hyperfunction using glottal airflow measures estimated from neck-surface acceleration (2018) (31)
- Semi-Supervised Biomedical Translation With Cycle Wasserstein Regression GANs (2018) (29)
- Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation (2018) (29)
- The Use of Autoencoders for Discovering Patient Phenotypes (2017) (27)
- A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI (2021) (27)
- Prediction using patient comparison vs. modeling: A case study for mortality prediction (2016) (26)
- Probabilistic Machine Learning for Healthcare (2020) (25)
- Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness (2021) (24)
- The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data (2019) (22)
- Problems in the deployment of machine-learned models in health care (2021) (19)
- Simultaneous Similarity-based Self-Distillation for Deep Metric Learning (2021) (19)
- The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations (2022) (19)
- Medical Dead-ends and Learning to Identify High-risk States and Treatments (2021) (16)
- A comprehensive EHR timeseries pre-training benchmark (2021) (16)
- The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech (2018) (16)
- CheXpert++: Approximating the CheXpert labeler for Speed, Differentiability, and Probabilistic Output (2020) (15)
- Topic Models for Mortality Modeling in Intensive Care Units (2012) (15)
- An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare (2020) (15)
- Turning the crank for machine learning: ease, at what expense? (2019) (14)
- Do no harm: a roadmap for responsible machine learning for health care (2019) (13)
- Improving the Fairness of Chest X-ray Classifiers (2022) (13)
- ClinicalVis: Supporting Clinical Task-Focused Design Evaluation (2018) (13)
- Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 (2018) (13)
- Ethical Machine Learning in Health (2020) (13)
- Sodium modelling to reduce intradialytic hypotension during haemodialysis for acute kidney injury in the intensive care unit (2016) (12)
- Understanding the Variance Collapse of SVGD in High Dimensions (2022) (12)
- Dear Watch, Should I Get a COVID-19 Test? Designing deployable machine learning for wearables (2021) (11)
- Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning (2021) (11)
- A comparison of approaches to improve worst-case predictive model performance over patient subpopulations (2021) (10)
- Author Correction: Do no harm: a roadmap for responsible machine learning for health care (2019) (9)
- If Influence Functions are the Answer, Then What is the Question? (2022) (9)
- Outcomes in patients with and without disability admitted to hospital with COVID-19: a retrospective cohort study (2022) (8)
- Multiple Sclerosis Severity Classification From Clinical Text (2020) (8)
- Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity (2022) (8)
- Confirming the themes and interpretive unity of Ghazal poetry using topic models (2013) (8)
- Ensuring machine learning for healthcare works for all (2020) (8)
- Methods and models for acute hypotensive episode prediction (2011) (7)
- Towards Characterizing the High-dimensional Bias of Kernel-based Particle Inference Algorithms (2019) (7)
- Counterfactually Guided Policy Transfer in Clinical Settings (2020) (7)
- S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning (2020) (6)
- Bayesian Trees for Automated Cytometry Data Analysis. (2019) (6)
- Racial Disparities and Mistrust in End-of-Life Care (2018) (6)
- Toward an objective aerodynamic assessment of vocal hyperfunction using a voice health monitor (2013) (5)
- Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning (2022) (5)
- Predicting Out-of-Domain Generalization with Local Manifold Smoothness (2022) (5)
- Improving Mutual Information Estimation with Annealed and Energy-Based Bounds (2023) (5)
- In medicine, how do we machine learn anything real? (2022) (5)
- Five principles for the intelligent use of AI in medical imaging (2021) (5)
- Preparing a Clinical Support Model for Silent Mode in General Internal Medicine (2020) (5)
- Machine learning and health need better values (2022) (5)
- Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation (2019) (5)
- FUTURE DIRECTIONS IN THE DEVELOPMENT OF AMBULATORY MONITORING FOR CLINICAL VOICE ASSESSMENT (2013) (4)
- Learning Optimal Predictive Checklists (2021) (4)
- Improving Dialogue Breakdown Detection with Semi-Supervised Learning (2020) (4)
- Medical imaging algorithms exacerbate biases in underdiagnosis (2021) (4)
- Uncovering Voice Misuse Using Symbolic Mismatch (2016) (4)
- Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays (2023) (3)
- Correction to: The role of machine learning in clinical research: transforming the future of evidence generation (2021) (3)
- Uniform Priors for Data-Efficient Transfer. (2020) (3)
- An Alternative to the Light Touch Digital Health Remote Study: The Stress and Recovery in Frontline COVID-19 Health Care Workers Study (2021) (3)
- Automatic Localization and Brand Detection of Cervical Spine Hardware on Radiographs Using Weakly Supervised Machine Learning. (2022) (3)
- TP53-mediated clonal hematopoiesis confers increased risk for incident atherosclerotic disease (2023) (3)
- Mitigating the impact of biased artificial intelligence in emergency decision-making (2022) (3)
- Write It Like You See It: Detectable Differences in Clinical Notes by Race Lead to Differential Model Recommendations (2022) (3)
- Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders (2019) (3)
- Tackling bias in AI health datasets through the STANDING Together initiative (2022) (3)
- Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing (2021) (2)
- When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction (2022) (2)
- Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study (2021) (2)
- Pre-Admission Use Of Selective Serotonin Reuptake Inhibitors Is Associated With ICU Mortality (2012) (2)
- Uniform Priors for Data-Efficient Learning (2022) (2)
- Get To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients (2022) (2)
- Confounding Feature Acquisition for Causal Effect Estimation (2020) (2)
- Semi-Markov Offline Reinforcement Learning for Healthcare (2022) (1)
- Decision-centered design of a clinical decision support system for acute management of pediatric congenital heart disease (2022) (1)
- General Co-Chairs (2022) (1)
- Evaluating and Improving Robustness of Self-Supervised Representations to Spurious Correlations (2022) (1)
- Metadata Correction: Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference (2015) (1)
- Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors (2022) (1)
- "Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts (2022) (1)
- Quantifying the Task-Specific Information in Text-Based Classifications (2021) (1)
- Visualization of Deep Models on Nursing Notes and Physiological Data for Predicting Health Outcomes Through Temporal Sliding Windows (2020) (1)
- Challenges of Differentially Private Prediction in Healthcare Settings (2020) (1)
- Change is Hard: A Closer Look at Subpopulation Shift (2023) (1)
- Modeling Mistrust in End-of-Life Care (2018) (1)
- Building A Research Partnership Between Computer Scientists and Health Service Researchers for Access and Analysis of Population-Level Health Datasets (2020) (0)
- An alternative to the ‘light touch’ digital health remote study: The Stress and Recovery in Frontline COVID-19 Healthcare Workers Study (Preprint) (2021) (0)
- Treating health disparities with artificial intelligence (2020) (0)
- Representation learning in multi-dimensional clinical timeseries for risk and event prediction (2017) (0)
- Conceptual Replicability (2019) (0)
- Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer Collaboration (2023) (0)
- Nailfold capillaroscopy and deep learning in diabetes (2023) (0)
- Transfer Learning vs. Batch Effects: what can we expect from neural networks in computational biology? (2019) (0)
- Prediction using patient comparison vs. modeling: a case study for mortality prediction. (2016) (0)
- Uniform Priors for Meta-Learning (2010) (0)
- Speaker-specific terms and resources (0)
- Subglottal ambulatory monitoring of vocal function to improve voice disorder assessment (2014) (0)
- Machine learning COVID-19 detection from wearables (2023) (0)
- Technical Replicability (2019) (0)
- Algorithmic Fairness in Chest X-ray Diagnosis: A Case Study (2023) (0)
- Motion sensitive detector (2008) (0)
- Sharing is Caring: Exploring machine learning-enabled methods for regional medical imaging exchange using procedure metadata. (2020) (0)
- 846: LONG-TERM OUTCOMES OF MINOR TROPONIN ELEVATIONS IN THE INTENSIVE CARE UNIT (2012) (0)
- Counterfactually Guided Off-policy Transfer in Clinical Settings (2020) (0)
- “Super-efficient gradient estimation technique,” Recent advances in efficient adjoint sensitivity analysis and its application in metamaterial design (2019) (0)
- U NDERSTANDING THE V ARIANCE C OLLAPSE OF SVGD IN H IGH D IMENSIONS (2022) (0)
- Patient characteristics, clinical care, resource use, and outcomes associated with hospitalization for COVID-19 in the Toronto area (2020) (0)
- AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT (preprint) (2023) (0)
- Building a research partnership between computer scientists and health service researchers for access and analysis of population-level health datasets: what are we learning? (2019) (0)
- Learning from Few Subjects with Large Amounts of Voice Monitoring Data (2019) (0)
- Digital healthcare tools for the world’s poorest people and places: A new framework for syndromic surveillance forged in the fight against COVID-19 in Somalia. (Preprint) (0)
- Reply to: ‘Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms’ and ‘Confounding factors need to be accounted for in assessing bias by machine learning algorithms’ (2022) (0)
- Statistical Replicability (2019) (0)
- INCORPORATING REAL-TIME BIOFEEDBACK CAPABILITIES INTO A VOICE HEALTH MONITOR (0)
- Corrections to “Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results For Vocal Fold Nodules” [Jun 14 1668-1675] (2015) (0)
- Making Health AI Work in the Real World: Strategies, innovations, and best practices for using AI to improve care delivery (2021) (0)
- Effect of clinical decision support systems on emergency medicine physicians' decision-making: A pilot scenario-based simulation study (2022) (0)
- 5 J un 2 01 8 Opportunities in Machine Learning for Healthcare (2018) (0)
- Machine Learning for Health ( ML 4 H )-What Parts of Healthcare are Ripe for Disruption by Machine Learning Right Now ? (0)
- Foundation Models in Healthcare: Opportunities, Risks & Strategies Forward (2023) (0)
- Real world relevance of generative counterfactual explanations (2022) (0)
- Correction: An Alternative to the Light Touch Digital Health Remote Study: The Stress and Recovery in Frontline COVID-19 Health Care Workers Study (2022) (0)
- What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask (Preprint) (2020) (0)
- Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning (2023) (0)
- Probabilistically Populated Medical Record Templates: Reducing Clinical Documentation Time Using Patient Cooperation (2013) (0)
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