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2020 – today
- 2023
[j9]Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi:
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)- [c53]Ming-Ying Yang
, Gloria Hyun-Jung Kwak, Tom J. Pollard, Leo Anthony Celi, Marzyeh Ghassemi:
Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit. AIES 2023: 333-350 - [c52]Anja Thieme, Aditya V. Nori, Marzyeh Ghassemi, Rishi Bommasani, Tariq Osman Andersen, Ewa Luger:
Foundation Models in Healthcare: Opportunities, Risks & Strategies Forward. CHI Extended Abstracts 2023: 512:1-512:4 - [c51]Qixuan Jin, Jacobien H. F. Oosterhoff, Yepeng Huang, Marzyeh Ghassemi, Gabriel A Brat:
Clinical Relevance Score for Guided Trauma Injury Pattern Discovery with Weakly Supervised β-VAE. CHIL 2023: 314-339 - [c50]Yuxin Xiao, Shulammite Lim, Tom Joseph Pollard, Marzyeh Ghassemi:
In the Name of Fairness: Assessing the Bias in Clinical Record De-identification. FAccT 2023: 123-137 - [c49]Vinith Menon Suriyakumar, Marzyeh Ghassemi, Berk Ustun:
When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction. ICML 2023: 33209-33228 - [c48]Yuzhe Yang, Haoran Zhang, Dina Katabi, Marzyeh Ghassemi:
Change is Hard: A Closer Look at Subpopulation Shift. ICML 2023: 39584-39622 - [c47]Haoran Zhang, Harvineet Singh, Marzyeh Ghassemi, Shalmali Joshi:
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts. ICML 2023: 41550-41578 - [i59]Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi:
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning. CoRR abs/2301.05664 (2023) - [i58]Yuzhe Yang, Haoran Zhang, Dina Katabi, Marzyeh Ghassemi:
Change is Hard: A Closer Look at Subpopulation Shift. CoRR abs/2302.12254 (2023) - [i57]Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger B. Grosse, Alireza Makhzani:
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds. CoRR abs/2303.06992 (2023) - [i56]Yuxin Xiao, Shulammite Lim, Tom Joseph Pollard, Marzyeh Ghassemi:
In the Name of Fairness: Assessing the Bias in Clinical Record De-identification. CoRR abs/2305.11348 (2023) - [i55]Ming-Ying Yang, Gloria Hyun-Jung Kwak, Tom J. Pollard, Leo Anthony Celi, Marzyeh Ghassemi:
Evaluating the Impact of Social Determinants on Health Prediction. CoRR abs/2305.12622 (2023) - [i54]Jiyoung Lee, Seungho Kim, Seunghyun Won, Joonseok Lee, Marzyeh Ghassemi, James Thorne, Jaeseok Choi, O.-Kil Kwon, Edward Choi:
VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception. CoRR abs/2308.01525 (2023) - [i53]Hyewon Jeong, Collin M. Stultz, Marzyeh Ghassemi:
Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals. CoRR abs/2308.04650 (2023) - 2022
- [j8]Azadeh Assadi, Peter C. Laussen, Gabrielle Freire, Marzyeh Ghassemi, Patricia Trbovich:
Decision-centered design of a clinical decision support system for acute management of pediatric congenital heart disease. Frontiers Digit. Health 4 (2022) - [j7]Marzyeh Ghassemi, Shakir Mohamed:
Machine learning and health need better values. npj Digit. Medicine 5 (2022) - [j6]Sarah M. Goodday, E. Karlin, A. Brooks, C. Chapman, Daniel R. Karlin, Luca Foschini, E. Kipping, M. Wildman, M. Francis, H. Greenman, Li Li, Eric E. Schadt, Marzyeh Ghassemi, Anna Goldenberg, Francesca Cormack, N. Taptiklis, C. Centen, S. Smith, Stephen H. Friend:
Better Understanding of the Metamorphosis of Pregnancy (BUMP): protocol for a digital feasibility study in women from preconception to postpartum. npj Digit. Medicine 5 (2022) - [j5]Marzyeh Ghassemi, Elaine O. Nsoesie:
In medicine, how do we machine learn anything real? Patterns 3(1): 100392 (2022) - [c46]Hammaad Adam, Ming-Ying Yang, Kenrick Cato, Ioana Baldini, Charles Senteio, Leo Anthony Celi, Jiaming Zeng, Moninder Singh, Marzyeh Ghassemi:
Write It Like You See It: Detectable Differences in Clinical Notes by Race Lead to Differential Model Recommendations. AIES 2022: 7-21 - [c45]Minfan Zhang, Daniel Ehrmann, Mjaye Mazwi, Danny Eytan, Marzyeh Ghassemi, Fanny Chevalier:
Get To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients. CHI 2022: 278:1-278:13 - [c44]Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi:
Counterfactually Guided Policy Transfer in Clinical Settings. CHIL 2022: 5-31 - [c43]Mehdi Fatemi, Mary Wu, Jeremy Petch, Walter Nelson, Stuart J. Connolly, Alexander Benz, Anthony Carnicelli, Marzyeh Ghassemi:
Semi-Markov Offline Reinforcement Learning for Healthcare. CHIL 2022: 119-137 - [c42]Haoran Zhang, Natalie Dullerud, Karsten Roth, Lauren Oakden-Rayner, Stephen Pfohl, Marzyeh Ghassemi:
Improving the Fairness of Chest X-ray Classifiers. CHIL 2022: 204-233 - [c41]Samarth Sinha, Karsten Roth, Anirudh Goyal, Marzyeh Ghassemi, Zeynep Akata, Hugo Larochelle, Animesh Garg:
Uniform Priors for Data-Efficient Learning. CVPR Workshops 2022: 4016-4027 - [c40]Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh, Thomas Hartvigsen, Frank Rudzicz, Marzyeh Ghassemi:
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations. FAccT 2022: 1194-1206 - [c39]Jimmy Ba, Murat A. Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang:
Understanding the Variance Collapse of SVGD in High Dimensions. ICLR 2022 - [c38]Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger Baker Grosse, Alireza Makhzani:
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds. ICLR 2022 - [c37]Natalie Dullerud, Karsten Roth, Kimia Hamidieh, Nicolas Papernot, Marzyeh Ghassemi:
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning. ICLR 2022 - [c36]Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse:
If Influence Functions are the Answer, Then What is the Question? NeurIPS 2022 - [i52]Mehdi Fatemi, Mary Wu, Jeremy Petch, Walter Nelson, Stuart J. Connolly, Alexander Benz, Anthony Carnicelli, Marzyeh Ghassemi:
Semi-Markov Offline Reinforcement Learning for Healthcare. CoRR abs/2203.09365 (2022) - [i51]Haoran Zhang, Natalie Dullerud, Karsten Roth, Lauren Oakden-Rayner, Stephen Robert Pfohl, Marzyeh Ghassemi:
Improving the Fairness of Chest X-ray Classifiers. CoRR abs/2203.12609 (2022) - [i50]Natalie Dullerud, Karsten Roth, Kimia Hamidieh, Nicolas Papernot, Marzyeh Ghassemi:
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning. CoRR abs/2203.12748 (2022) - [i49]Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh, Thomas Hartvigsen, Frank Rudzicz, Marzyeh Ghassemi:
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations. CoRR abs/2205.03295 (2022) - [i48]Hammaad Adam, Ming-Ying Yang, Kenrick Cato, Ioana Baldini, Charles Senteio, Leo Anthony Celi, Jiaming Zeng, Moninder Singh, Marzyeh Ghassemi:
Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations. CoRR abs/2205.03931 (2022) - [i47]Vinith M. Suriyakumar, Marzyeh Ghassemi, Berk Ustun:
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction. CoRR abs/2206.02058 (2022) - [i46]Nathan Ng, Neha Hulkund, Kyunghyun Cho, Marzyeh Ghassemi:
Predicting Out-of-Domain Generalization with Local Manifold Smoothness. CoRR abs/2207.02093 (2022) - [i45]Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse:
If Influence Functions are the Answer, Then What is the Question? CoRR abs/2209.05364 (2022) - [i44]Haoran Zhang, Harvineet Singh, Marzyeh Ghassemi, Shalmali Joshi:
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts. CoRR abs/2210.10769 (2022) - [i43]Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, Marzyeh Ghassemi:
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors. CoRR abs/2211.11031 (2022) - 2021
- [j4]Susanne Gaube, Harini Suresh, Martina Raue, Alexander Merritt, Seth J. Berkowitz, Eva Lermer, Joseph F. Coughlin, John V. Guttag, Errol Colak, Marzyeh Ghassemi:
Do as AI say: susceptibility in deployment of clinical decision-aids. npj Digit. Medicine 4 (2021) - [c35]Suchi Saria, Marzyeh Ghassemi, Ziad Obermeyer, Karandeep Singh, Pei-Yun S. Hsueh, Eric J. Topol:
Making Health AI Work in the Real World: Strategies, innovations, and best practices for using AI to improve care delivery. AMIA 2021 - [c34]Matthew B. A. McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, Marzyeh Ghassemi:
A comprehensive EHR timeseries pre-training benchmark. CHIL 2021: 257-278 - [c33]Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, Marzyeh Ghassemi:
An empirical framework for domain generalization in clinical settings. CHIL 2021: 279-290 - [c32]Sindhu C. M. Gowda, Shalmali Joshi, Haoran Zhang, Marzyeh Ghassemi:
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing. CIKM 2021: 606-616 - [c31]Victoria Cheng, Vinith M. Suriyakumar, Natalie Dullerud, Shalmali Joshi, Marzyeh Ghassemi:
Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness. FAccT 2021: 149-160 - [c30]Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi:
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. FAccT 2021: 723-734 - [c29]Karsten Roth, Timo Milbich, Björn Ommer, Joseph Paul Cohen, Marzyeh Ghassemi:
Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. ICML 2021: 9095-9106 - [c28]Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi:
Learning Optimal Predictive Checklists. NeurIPS 2021: 1215-1229 - [c27]Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi:
Medical Dead-ends and Learning to Identify High-Risk States and Treatments. NeurIPS 2021: 4856-4870 - [c26]Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Björn Ommer:
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. NeurIPS 2021: 25006-25018 - [c25]Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew B. A. McDermott, Irene Y. Chen, Marzyeh Ghassemi:
CheXclusion: Fairness gaps in deep chest X-ray classifiers. PSB 2021 - [e2]Marzyeh Ghassemi, Tristan Naumann, Emma Pierson:
ACM CHIL '21: ACM Conference on Health, Inference, and Learning, Virtual Event, USA, April 8-9, 2021. ACM 2021, ISBN 978-1-4503-8359-2 [contents] - [i42]Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, Marzyeh Ghassemi:
An Empirical Framework for Domain Generalization in Clinical Settings. CoRR abs/2103.11163 (2021) - [i41]Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Björn Ommer:
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. CoRR abs/2107.09562 (2021) - [i40]Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, Matthew P. Lungren, Lyle J. Palmer, Brandon J. Price, Saptarshi Purkayastha, Ayis Pyrros, Luke Oakden-Rayner, Chima Okechukwu, Laleh Seyyed-Kalantari, Hari Trivedi, Ryan Wang, Zachary Zaiman, Haoran Zhang, Judy W. Gichoya:
Reading Race: AI Recognises Patient's Racial Identity In Medical Images. CoRR abs/2107.10356 (2021) - [i39]Stephen R. Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H. Shah:
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations. CoRR abs/2108.12250 (2021) - [i38]Sindhu C. M. Gowda, Shalmali Joshi, Haoran Zhang, Marzyeh Ghassemi:
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing. CoRR abs/2108.12510 (2021) - [i37]Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi:
Medical Dead-ends and Learning to Identify High-risk States and Treatments. CoRR abs/2110.04186 (2021) - [i36]Zining Zhu, Aparna Balagopalan, Marzyeh Ghassemi, Frank Rudzicz:
Quantifying the Task-Specific Information in Text-Based Classifications. CoRR abs/2110.08931 (2021) - [i35]Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi:
Learning Optimal Predictive Checklists. CoRR abs/2112.01020 (2021) - 2020
- [c24]Alister D. Costa, Stefan Denkovski, Michal Malyska, Sae Young Moon, Brandon Rufino, Zhen Yang, Taylor W. Killian, Marzyeh Ghassemi:
Multiple Sclerosis Severity Classification From Clinical Text. ClinicalNLP@EMNLP 2020: 7-23 - [c23]Haoran Zhang, Amy X. Lu, Mohamed Abdalla, Matthew B. A. McDermott, Marzyeh Ghassemi:
Hurtful words: quantifying biases in clinical contextual word embeddings. CHIL 2020: 110-120 - [c22]Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C. Hughes, Tristan Naumann:
MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III. CHIL 2020: 222-235 - [c21]Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi:
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness. EMNLP (1) 2020: 1268-1283 - [c20]Matthew B. A. McDermott, Tzu-Ming Harry Hsu, Wei-Hung Weng, Marzyeh Ghassemi, Peter Szolovits:
CheXpert++: Approximating the CheXpert Labeler for Speed, Differentiability, and Probabilistic Output. MLHC 2020: 913-927 - [c19]Bret Nestor, Liam G. McCoy, Amol Verma, Chloé Pou-Prom, Joshua Murray, Sebnem Kuzulugil, David Dai, Muhammad Mamdani, Anna Goldenberg, Marzyeh Ghassemi:
Preparing a Clinical Support Model for Silent Mode in General Internal Medicine. MLHC 2020: 950-972 - [c18]Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Marzyeh Ghassemi:
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare. ML4H@NeurIPS 2020: 139-160 - [c17]Shirly Wang, Seung Eun Yi, Shalmali Joshi, Marzyeh Ghassemi:
Confounding Feature Acquisition for Causal Effect Estimation. ML4H@NeurIPS 2020: 379-396 - [e1]Marzyeh Ghassemi:
ACM CHIL '20: ACM Conference on Health, Inference, and Learning, Toronto, Ontario, Canada, April 2-4, 2020 [delayed]. ACM 2020, ISBN 978-1-4503-7046-2 [contents] - [i34]Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew B. A. McDermott, Marzyeh Ghassemi:
CheXclusion: Fairness gaps in deep chest X-ray classifiers. CoRR abs/2003.00827 (2020) - [i33]Haoran Zhang, Amy X. Lu, Mohamed Abdalla, Matthew B. A. McDermott, Marzyeh Ghassemi:
Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings. CoRR abs/2003.11515 (2020) - [i32]Joseph Paul Cohen, Lan Dao, Paul Morrison, Karsten Roth, Yoshua Bengio, Beiyi Shen, Almas Abbasi, Mahsa Hoshmand-Kochi, Marzyeh Ghassemi, Haifang Li, Tim Q. Duong:
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning. CoRR abs/2005.11856 (2020) - [i31]Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi:
Counterfactually Guided Policy Transfer in Clinical Settings. CoRR abs/2006.11654 (2020) - [i30]Joseph Paul Cohen, Paul Morrison, Lan Dao, Karsten Roth, Tim Q. Duong, Marzyeh Ghassemi:
COVID-19 Image Data Collection: Prospective Predictions Are the Future. CoRR abs/2006.11988 (2020) - [i29]Matthew B. A. McDermott, Tzu-Ming Harry Hsu, Wei-Hung Weng, Marzyeh Ghassemi, Peter Szolovits:
CheXpert++: Approximating the CheXpert labeler for Speed, Differentiability, and Probabilistic Output. CoRR abs/2006.15229 (2020) - [i28]Matthew B. A. McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, Marzyeh Ghassemi:
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data. CoRR abs/2007.10185 (2020) - [i27]Karsten Roth, Timo Milbich, Björn Ommer, Joseph Paul Cohen, Marzyeh Ghassemi:
S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. CoRR abs/2009.08348 (2020) - [i26]Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi:
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness. CoRR abs/2009.10195 (2020) - [i25]Irene Y. Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, Marzyeh Ghassemi:
Ethical Machine Learning in Health Care. CoRR abs/2009.10576 (2020) - [i24]Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath:
Probabilistic Machine Learning for Healthcare. CoRR abs/2009.11087 (2020) - [i23]Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi:
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. CoRR abs/2010.06667 (2020) - [i22]Alister D. Costa, Stefan Denkovski, Michal Malyska, Sae Young Moon, Brandon Rufino, Zhen Yang, Taylor W. Killian, Marzyeh Ghassemi:
Multiple Sclerosis Severity Classification From Clinical Text. CoRR abs/2010.15316 (2020) - [i21]Nathan Ng, Marzyeh Ghassemi, Narendran Thangarajan, Jiacheng Pan, Qi Guo:
Improving Dialogue Breakdown Detection with Semi-Supervised Learning. CoRR abs/2011.00136 (2020) - [i20]Shirly Wang, Seung Eun Yi, Shalmali Joshi, Marzyeh Ghassemi:
Confounding Feature Acquisition for Causal Effect Estimation. CoRR abs/2011.08753 (2020) - [i19]Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Marzyeh Ghassemi:
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare. CoRR abs/2011.11235 (2020)
2010 – 2019
- 2019
- [c16]Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini:
Reproducibility in Machine Learning for Health. RML@ICLR 2019 - [c15]Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew B. A. McDermott, Willie Boag, Wei-Hung Weng, Peter Szolovits, Marzyeh Ghassemi:
Clinically Accurate Chest X-Ray Report Generation. MLHC 2019: 249-269 - [c14]Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. MLHC 2019: 381-405 - [c13]Jose Javier Gonzalez Ortiz, Daryush D. Mehta, Jarrad H. Van Stan, Robert E. Hillman, John V. Guttag, Marzyeh Ghassemi:
Learning from Few Subjects with Large Amounts of Voice Monitoring Data. MLHC 2019: 704-720 - [c12]Aparna Balagopalan, Jekaterina Novikova, Matthew B. A. McDermott, Bret Nestor, Tristan Naumann, Marzyeh Ghassemi:
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation. ML4H@NeurIPS 2019: 202-219 - [c11]Alex X. Lu, Amy X. Lu, Wiebke Schormann, Marzyeh Ghassemi, David W. Andrews, Alan M. Moses:
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers. NeurIPS 2019: 1852-1860 - [i18]Denny Wu, Hirofumi Kobayashi, Charles Ding, Cheng Lei, Keisuke Goda, Marzyeh Ghassemi:
Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders. CoRR abs/1901.06618 (2019) - [i17]Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew B. A. McDermott, Willie Boag, Wei-Hung Weng, Peter Szolovits, Marzyeh Ghassemi:
Clinically Accurate Chest X-Ray Report Generation. CoRR abs/1904.02633 (2019) - [i16]Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini:
Reproducibility in Machine Learning for Health. CoRR abs/1907.01463 (2019) - [i15]Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Michael C. Hughes, Tristan Naumann, Marzyeh Ghassemi:
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III. CoRR abs/1907.08322 (2019) - [i14]Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. CoRR abs/1908.00690 (2019) - [i13]Aparna Balagopalan, Jekaterina Novikova, Matthew B. A. McDermott, Bret Nestor, Tristan Naumann, Marzyeh Ghassemi:
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation. CoRR abs/1912.04370 (2019) - 2018
- [c10]Matthew B. A. McDermott, Tom Yan, Tristan Naumann, Nathan Hunt, Harini Suresh, Peter Szolovits, Marzyeh Ghassemi:
Semi-Supervised Biomedical Translation With Cycle Wasserstein Regression GANs. AAAI 2018: 2363-2370 - [c9]Willie Boag, Harini Suresh, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi:
Racial Disparities and Mistrust in End-of-Life Care. MLHC 2018: 587-602 - [i12]Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Rajesh Ranganath:
Opportunities in Machine Learning for Healthcare. CoRR abs/1806.00388 (2018) - [i11]Willie Boag, Harini Suresh, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi:
Modeling Mistrust in End-of-Life Care. CoRR abs/1807.00124 (2018) - [i10]Marzyeh Ghassemi, Mahima Pushkarna, James Wexler, Jesse Johnson, Paul Varghese:
ClinicalVis: Supporting Clinical Task-Focused Design Evaluation. CoRR abs/1810.05798 (2018) - [i9]Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian V. Dalca, Samuel G. Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew B. A. McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi:
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018. CoRR abs/1811.07216 (2018) - [i8]Aparna Balagopalan, Jekaterina Novikova, Frank Rudzicz, Marzyeh Ghassemi:
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech. CoRR abs/1811.12254 (2018) - [i7]Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation. CoRR abs/1811.12583 (2018) - 2017
- [b1]Marzyeh Ghassemi:
Representation learning in multi-dimensional clinical timeseries for risk and event prediction. Massachusetts Institute of Technology, Cambridge, USA, 2017 - [j3]Mike Wu, Marzyeh Ghassemi, Mengling Feng, Leo A. Celi, Peter Szolovits, Finale Doshi-Velez:
Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database. J. Am. Medical Informatics Assoc. 24(3): 488-495 (2017) - [c8]Marzyeh Ghassemi, Mike Wu, Michael C. Hughes, Peter Szolovits, Finale Doshi-Velez:
Predicting intervention onset in the ICU with switching state space models. CRI 2017 - [c7]Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi:
Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach. MLHC 2017: 147-163 - [c6]Harini Suresh, Nathan Hunt, Alistair E. W. Johnson, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi:
Clinical Intervention Prediction and Understanding with Deep Neural Networks. MLHC 2017: 322-337 - [i6]Harini Suresh, Peter Szolovits, Marzyeh Ghassemi:
The Use of Autoencoders for Discovering Patient Phenotypes. CoRR abs/1703.07004 (2017) - [i5]Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi:
Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach. CoRR abs/1705.08422 (2017) - [i4]Harini Suresh, Nathan Hunt, Alistair E. W. Johnson, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi:
Clinical Intervention Prediction and Understanding using Deep Networks. CoRR abs/1705.08498 (2017) - [i3]Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed, Leo A. Celi, Peter Szolovits, Marzyeh Ghassemi:
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