Papers

Vinith M Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi (2021). Challenges of Differentially Private Prediction in Healthcare Settings.

Isaac S Kohane, Bruce J Aronow, Paul Avillach, Brett K Beaulieu-Jones, Riccardo Bellazzi, Robert L Bradford, Gabriel A Brat, Mario Cannataro, James J Cimino, Noelia Garcı́a-Barrio, others (2021). What every reader should know about studies using electronic health record data but may be afraid to ask. Journal of medical Internet research.

Jienan Yao, Yuyang Liu, Brenna Li, Stephen Gou, Chloe Pou-Prom, Joshua Murray, Amol Verma, Muhammad Mamdani, Marzyeh Ghassemi (2021). Visualization of deep models on nursing notes and physiological data for predicting health outcomes through temporal sliding windows. Explainable AI in Healthcare and Medicine.

Laleh Seyyed-Kalantari, Haoran Zhang, Matthew McDermott, Irene Y Chen, Marzyeh Ghassemi (2021). Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature Medicine.

E Hope Weissler, Tristan Naumann, Tomas Andersson, Rajesh Ranganath, Olivier Elemento, Yuan Luo, Daniel F Freitag, James Benoit, Michael C Hughes, Faisal Khan, others (2021). The role of machine learning in clinical research: transforming the future of evidence generation. Trials.

Marzyeh Ghassemi, Luke Oakden-Rayner, Andrew L Beam (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health.

Karsten Roth, Timo Milbich, Bjorn Ommer, Joseph Paul Cohen, Marzyeh Ghassemi (2021). Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. International Conference on Machine Learning.

Matthew BA McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Luca Foschini, Marzyeh Ghassemi (2021). Reproducibility in machine learning for health research: Still a ways to go. Science Translational Medicine.

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, others (2021). Reading Race: AI Recognises Patient's Racial Identity In Medical Images. arXiv preprint arXiv:2107.10356.

Sindhu Gowda, Shalmali Joshi, Haoran Zhang, Marzyeh Ghassemi (2021). Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing. arXiv preprint arXiv:2108.12510.

Joseph Paul Cohen, Tianshi Cao, Joseph D Viviano, Chin-Wei Huang, Michael Fralick, Marzyeh Ghassemi, Muhammad Mamdani, Russell Greiner, Yoshua Bengio (2021). Problems in the deployment of machine-learned models in health care. CMAJ.

Seung Eun Yi, Vinyas Harish, Jahir M Gutierrez, Mathieu Ravaut, Kathy Kornas, Tristan Watson, Tomi Poutanen, Marzyeh Ghassemi, Maksims Volkovs, Laura Rosella (2021). Predicting hospitalizations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study. medRxiv.

Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Chen, Marzyeh Ghassemi (2021). Medical imaging algorithms exacerbate biases in underdiagnosis.

Mehdi Fatemi, Taylor W Killian, Jayakumar Subramanian, Marzyeh Ghassemi (2021). Medical Dead-ends and Learning to Identify High-risk States and Treatments. Advances in Neural Information Processing Systems.

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Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi (2021). Learning Optimal Predictive Checklists. Advances in Neural Information Processing Systems.

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Amol A Verma, Joshua Murray, Russell Greiner, Joseph Paul Cohen, Kaveh G Shojania, Marzyeh Ghassemi, Sharon E Straus, Chloe Pou-Prom, Muhammad Mamdani (2021). Implementing machine learning in medicine. CMAJ.

Errol Colak, Robert Moreland, Marzyeh Ghassemi (2021). Five principles for the intelligent use of AI in medical imaging. Springer.

Judy Wawira Gichoya, Liam G McCoy, Leo Anthony Celi, Marzyeh Ghassemi (2021). Equity in essence: a call for operationalising fairness in machine learning for healthcare. BMJ Health & Care Informatics.

Susanne Gaube, Harini Suresh, Martina Raue, Alexander Merritt, Seth J Berkowitz, Eva Lermer, Joseph F Coughlin, John V Guttag, Errol Colak, Marzyeh Ghassemi (2021). Do as AI say: susceptibility in deployment of clinical decision-aids. NPJ digital medicine.

Bret Nestor, Jaryd Hunter, Raghu Kainkaryam, Erik Drysdale, Jeffrey B Inglis, Allison Shapiro, Sujay Nagaraj, Marzyeh Ghassemi, Luca Foschini, Anna Goldenberg (2021). Dear Watch, Should I Get a COVID-19 Test? Designing deployable machine learning for wearables. medRxiv.

Vinith M Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi (2021). Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.

Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Björn Ommer (2021). Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. arXiv preprint arXiv:2107.09562.

Amol A Verma, Tejasvi Hora, Hae Young Jung, Michael Fralick, Sarah L Malecki, Lauren Lapointe-Shaw, Adina Weinerman, Terence Tang, Janice L Kwan, Jessica J Liu, others (2021). Characteristics and outcomes of hospital admissions for COVID-19 and influenza in the Toronto area. CMAJ.

Amol A Verma, Tejasvi Hora, Hae Young Jung, Michael Fralick, Sarah L Malecki, Lauren Lapointe-Shaw, Adina Weinerman, Terence Tang, Janice L Kwan, Jessica J Liu, others (2021). Caractéristiques et issues des hospitalisations pour les cas de COVID-19 et d’influenza dans la région de Toronto. CMAJ.

Victoria Cheng, Vinith M Suriyakumar, Natalie Dullerud, Shalmali Joshi, Marzyeh Ghassemi (2021). Can You Fake It Until You Make It? Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.

Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, Marzyeh Ghassemi (2021). An empirical framework for domain generalization in clinical settings. Proceedings of the Conference on Health, Inference, and Learning.

Stephen R Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H Shah (2021). A comparison of approaches to improve worst-case predictive model performance over patient subpopulations. arXiv preprint arXiv:2108.12250.

Samarth Sinha, Karsten Roth, Anirudh Goyal, Marzyeh Ghassemi, Hugo Larochelle, Animesh Garg (2020). Uniform Priors for Data-Efficient Transfer. arXiv preprint arXiv:2006.16524.

Irene Y Chen, Shalmali Joshi, Marzyeh Ghassemi (2020). Treating health disparities with artificial intelligence. Nature medicine.

Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi (2020). Ssmba: Self-supervised manifold based data augmentation for improving out-of-domain robustness. arXiv preprint arXiv:2009.10195.

Amy X Lu, Haoran Zhang, Marzyeh Ghassemi, Alan M Moses (2020). Self-supervised contrastive learning of protein representations by mutual information maximization. BioRxiv.

Karsten Roth, Timo Milbich, Björn Ommer, Joseph Paul Cohen, Marzyeh Ghassemi (2020). S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. arXiv preprint arXiv:2009.08348.

Irene Y Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath (2020). Probabilistic machine learning for healthcare. Annual Review of Biomedical Data Science.

Bret Nestor, Liam G McCoy, Amol Verma, Chloe Pou-Prom, Joshua Murray, Sebnem Kuzulugil, David Dai, Muhammad Mamdani, Anna Goldenberg, Marzyeh Ghassemi (2020). Preparing a Clinical Support Model for Silent Mode in General Internal Medicine. Machine Learning for Healthcare Conference.

Joseph Paul Cohen, Lan Dao, Karsten Roth, Paul Morrison, Yoshua Bengio, Almas F Abbasi, Beiyi Shen, Hoshmand Kochi Mahsa, Marzyeh Ghassemi, Haifang Li, others (2020). Predicting covid-19 pneumonia severity on chest x-ray with deep learning. Cureus.

Amol Verma, Tejasvi Hora, Hae Young Jung, Michael Fralick, Sarah Malecki, Lauren Lapointe-Shaw, Adina Weinerman, Terence Tang, Janice Kwan, Jessica J Liu, others (2020). Patient characteristics, clinical care, resource use, and outcomes associated with hospitalization for COVID-19 in the Toronto area. medRxiv.

Alister D Costa, Stefan Denkovski, Michal Malyska, Sae Young Moon, Brandon Rufino, Zhen Yang, Taylor Killian, Marzyeh Ghassemi (2020). Multiple sclerosis severity classification from clinical text. arXiv preprint arXiv:2010.15316.

Shirly Wang, Matthew BA McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C Hughes, Tristan Naumann (2020). Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii. Proceedings of the ACM Conference on Health, Inference, and Learning.

Nathan Ng, Marzyeh Ghassemi, Narendran Thangarajan, Jiacheng Pan, Qi Guo (2020). Improving Dialogue Breakdown Detection with Semi-Supervised Learning. arXiv preprint arXiv:2011.00136.

Haoran Zhang, Amy X Lu, Mohamed Abdalla, Matthew McDermott, Marzyeh Ghassemi (2020). Hurtful words: quantifying biases in clinical contextual word embeddings. proceedings of the ACM Conference on Health, Inference, and Learning.

Irene Y Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, Marzyeh Ghassemi (2020). Ethical Machine Learning in Healthcare. Annual Review of Biomedical Data Science.

Liam G McCoy, John D Banja, Marzyeh Ghassemi, Leo Anthony Celi (2020). Ensuring machine learning for healthcare works for all. BMJ Health & Care Informatics.

Aparna Balagopalan, Jekaterina Novikova, Matthew BA Mcdermott, Bret Nestor, Tristan Naumann, Marzyeh Ghassemi (2020). Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation. Machine Learning for Health Workshop.

Joseph Paul Cohen, Paul Morrison, Lan Dao, Karsten Roth, Tim Q Duong, Marzyeh Ghassemi (2020). Covid-19 image data collection: Prospective predictions are the future. arXiv preprint arXiv:2006.11988.

Taylor Killian, Marzyeh Ghassemi, Shalmali Joshi (2020). Counterfactually guided policy transfer in clinical settings. arXiv preprint arXiv:2006.11654.

Shirly Wang, Seung Eun Yi, Shalmali Joshi, Marzyeh Ghassemi (2020). Confounding Feature Acquisition for Causal Effect Estimation. Machine Learning for Health.

Matthew BA McDermott, Tzu Ming Harry Hsu, Wei-Hung Weng, Marzyeh Ghassemi, Peter Szolovits (2020). Chexpert++: Approximating the chexpert labeler for speed, differentiability, and probabilistic output. Machine Learning for Healthcare Conference.

Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Y Chen, Marzyeh Ghassemi (2020). CheXclusion: Fairness gaps in deep chest X-ray classifiers. BIOCOMPUTING 2021: Proceedings of the Pacific Symposium.

Andrew L Beam, Arjun K Manrai, Marzyeh Ghassemi (2020). Challenges to the reproducibility of machine learning models in health care. Jama.

Laura C Rosella, Ajay Agrawal, Phillip Awadalla, Adalsteinn Brown, Jeffrey Brook, Lori Diemert, France Gagnon, Marzyeh Ghassemi, Avi Goldfarb, Rick Glazier, others (2020). Building a data science platform for better health.

Taylor Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Marzyeh Ghassemi (2020). An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare. arXiv preprint arXiv:2011.11235.

Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, Irene Y Chen, Rajesh Ranganath (2020). A review of challenges and opportunities in machine learning for health. AMIA Summits on Translational Science Proceedings.

Matthew McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, Marzyeh Ghassemi (2020). A comprehensive evaluation of multi-task learning and multi-task pre-training on EHR time-series data. arXiv preprint arXiv:2007.10185.

Laleh S Kalantari, Mohamed Bakr, Marzyeh Ghassemi (2019). “Super-efficient gradient estimation technique,” Recent advances in efficient adjoint sensitivity analysis and its application in metamaterial design. The Journal of the Acoustical Society of America.

Tom J Pollard, Irene Chen, Jenna Wiens, Steven Horng, Danny Wong, Marzyeh Ghassemi, Heather Mattie, Emily Lindemer, Trishan Panch (2019). Turning the crank for machine learning: ease, at what expense?. The Lancet Digital Health.

Jimmy Ba, Murat A Erdogdu, Marzyeh Ghassemi, Taiji Suzuki, Shengyang Sun, Denny Wu, Tianzong Zhang (2019). Towards characterizing the high-dimensional bias of kernel-based particle inference algorithms.

Leo A Celi, Luca Citi, Marzyeh Ghassemi, Tom J Pollard (2019). The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data. PloS one.

Alex X Lu, Amy X Lu, Wiebke Schormann, Marzyeh Ghassemi, David W Andrews, Alan M Moses (2019). The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers. arXiv preprint arXiv:1906.07282.

Matthew McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini (2019). Reproducibility in machine learning for health. arXiv preprint arXiv:1907.01463.

Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, Irene Y Chen, Rajesh Ranganath (2019). Practical guidance on artificial intelligence for health-care data. The Lancet Digital Health.

Denny Wu, Hirofumi Kobayashi, Charles Ding, Lei Cheng, Keisuke Goda Marzyeh Ghassemi (2019). Modeling the biological pathology continuum with hsic-regularized wasserstein auto-encoders. arXiv preprint arXiv:1901.06618.

Bret Nestor, Matthew BA McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C Hughes, Anna Goldenberg, Marzyeh Ghassemi (2019). Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks. Machine Learning for Healthcare Conference.

Jenna Wiens, Suchi Saria, Mark Sendak, Marzyeh Ghassemi, Vincent X Liu, Finale Doshi-Velez, Kenneth Jung, Katherine Heller, David Kale, Mohammed Saeed, others (2019). Do no harm: a roadmap for responsible machine learning for health care. Nature medicine.

Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew McDermott, Willie Boag, Wei-Hung Weng, Peter Szolovits, Marzyeh Ghassemi (2019). Clinically accurate chest x-ray report generation. Machine Learning for Healthcare Conference.

Irene Y Chen, Peter Szolovits, Marzyeh Ghassemi (2019). Can AI help reduce disparities in general medical and mental health care?. AMA journal of ethics.

Michael Schull, Michael Brudno, Marzyeh Ghassemi, Garth Gibson, Anna Goldenbrg, Alison Paprica, Laura Rosella, Therese Stukel, Charles Victor, Carl Virtanen (2019). Building a research partnership between computer scientists and health service researchers for access and analysis of population-level health datasets: what are we learning?. International Journal of Population Data Science.

Aparna Balagopalan, Jekaterina Novikova, Frank Rudzicz, Marzyeh Ghassemi (2018). The effect of heterogeneous data for Alzheimer's disease detection from speech. arXiv preprint arXiv:1811.12254.

Matthew McDermott, Tom Yan, Tristan Naumann, Nathan Hunt, Harini Suresh, Peter Szolovits, Marzyeh Ghassemi (2018). Semi-supervised biomedical translation with cycle wasserstein regression GANs. Proceedings of the AAAI Conference on Artificial Intelligence.

Bret Nestor, Matthew McDermott, Geeticka Chauhan, Tristan Naumann, Michael C Hughes, Anna Goldenberg, Marzyeh Ghassemi (2018). Rethinking clinical prediction: why machine learning must consider year of care and feature aggregation. arXiv preprint arXiv:1811.12583.

Willie Boag, Harini Suresh, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi (2018). Racial disparities and mistrust in end-of-life care. Machine Learning for Healthcare Conference.

Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, Rajesh Ranganath (2018). Opportunities in machine learning for healthcare. arXiv preprint arXiv:1806.00388.

Willie Boag, Harini Suresh, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi (2018). Modeling mistrust in end-of-life care. arXiv preprint arXiv:1807.00124.

Natalia Antropova, Andrew L Beam, Brett K Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian Dalca, Sam Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, others (2018). Machine learning for health (ML4H) workshop at NeurIPS 2018. arXiv preprint arXiv:1811.07216.

Marzyeh Ghassemi, Mahima Pushkarna, James Wexler, Jesse Johnson, Paul Varghese (2018). Clinicalvis: Supporting clinical task-focused design evaluation. arXiv preprint arXiv:1810.05798.

Juan P Cortés, Vı́ctor M Espinoza, Marzyeh Ghassemi, Daryush D Mehta, Jarrad H Van Stan, Robert E Hillman, John V Guttag, Mat\ás Zañartu (2018). Ambulatory assessment of phonotraumatic vocal hyperfunction using glottal airflow measures estimated from neck-surface acceleration. PLoS One.

Mike Wu, Marzyeh Ghassemi, Mengling Feng, Leo A Celi, Peter Szolovits, Finale Doshi-Velez (2017). Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database. Journal of the American Medical Informatics Association.

Harini Suresh, Peter Szolovits, Marzyeh Ghassemi (2017). The use of autoencoders for discovering patient phenotypes. arXiv preprint arXiv:1703.07004.

Marzyeh Ghassemi (2017). Representation learning in multi-dimensional clinical timeseries for risk and event prediction.

Marzyeh Ghassemi, Mike Wu, Michael C Hughes, Peter Szolovits, Finale Doshi-Velez (2017). Predicting intervention onset in the ICU with switching state space models. AMIA Summits on Translational Science Proceedings.

Marzyeh Ghassemi, Harini Suresh, Nathan Hunt, Tristan Naumann, Leo Anthony Celi, Peter Szolovits (2017). Learning Clinical Events.

Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed, Leo Celi, Peter Szolovits, Marzyeh Ghassemi (2017). Deep reinforcement learning for sepsis treatment. arXiv preprint arXiv:1711.09602.

Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi (2017). Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach. Machine Learning for Healthcare Conference.

Harini Suresh, Nathan Hunt, Alistair Johnson, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi (2017). Clinical intervention prediction and understanding with deep neural networks. Machine Learning for Healthcare Conference.

Marzyeh Ghassemi, Zeeshan Syed, Daryush Mehta, Jarrad Van Stan, Robert Hillman, John Guttag (2016). Uncovering voice misuse using symbolic mismatch. Machine Learning for Healthcare Conference.

Katherine E Lynch, Fatimah Ghassemi, Jennifer E Flythe, Mengling Feng, Marzyeh Ghassemi, Leo Anthony Celi, Steven M Brunelli (2016). Sodium modelling to reduce intradialytic hypotension during haemodialysis for acute kidney injury in the intensive care unit. Nephrology.

Mark Hoogendoorn, Ali El Hassouni, Kwongyen Mok, Marzyeh Ghassemi, Peter Szolovits (2016). Prediction using patient comparison vs. modeling: A case study for mortality prediction. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Anna Rumshisky, Marzyeh Ghassemi, Tristan Naumann, Peter Szolovits, VM Castro, TH McCoy, RH Perlis (2016). Predicting early psychiatric readmission with natural language processing of narrative discharge summaries. Translational psychiatry.

Jesse D Raffa, Marzyeh Ghassemi, Tristan Naumann, Mengling Feng, Douglas Hsu (2016). Data Analysis. Secondary Analysis of Electronic Health Records.

Daryush D Mehta, Jarrad H Van Stan, Matı́as Zañartu, Marzyeh Ghassemi, John V Guttag, V̧́tor M Espinoza, Juan P Cortés, Harold A Cheyne, Robert E Hillman (2015). Using ambulatory voice monitoring to investigate common voice disorders: Research update. Frontiers in bioengineering and biotechnology.

Marzyeh Ghassemi, Leo Anthony Celi, David J Stone (2015). State of the art review: the data revolution in critical care. Critical Care.

Maggie Makar, Marzyeh Ghassemi, David M Cutler, Ziad Obermeyer (2015). Short-term mortality prediction for elderly patients using Medicare claims data. International journal of machine learning and computing.

Omar Badawi, Thomas Brennan, Leo Anthony Celi, Mengling Feng, Marzyeh Ghassemi, Andrea Ippolito, Alistair Johnson, Roger G Mark, Louis Mayaud, George Moody, others (2015). Metadata correction: making big data useful for health care: a summary of the inaugural mit critical data conference. JMIR medical informatics.

Marzyeh Ghassemi, Marco Pimentel, Tristan Naumann, Thomas Brennan, David Clifton, Peter Szolovits, Mengling Feng (2015). A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data. Proceedings of the AAAI conference on artificial intelligence.

Marzyeh Ghassemi, Tristan Naumann, Finale Doshi-Velez, Nicole Brimmer, Rohit Joshi, Anna Rumshisky, Peter Szolovits (2014). Unfolding physiological state: Mortality modelling in intensive care units. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining.

Robert E Hillman, Daryush Mehta, Jarrad H Van Stan, Matias Zanartu, Marzyeh Ghassemi, John V Guttag (2014). Subglottal ambulatory monitoring of vocal function to improve voice disorder assessment. The Journal of the Acoustical Society of America.

Omar Badawi, Thomas Brennan, Leo Anthony Celi, Mengling Feng, Marzyeh Ghassemi, Andrea Ippolito, Alistair Johnson, Roger G Mark, Louis Mayaud, George Moody, others (2014). Making big data useful for health care: a summary of the inaugural mit critical data conference. JMIR medical informatics.

Marzyeh Ghassemi, John Marshall, Nakul Singh, David J Stone, Leo Anthony Celi (2014). Leveraging a critical care database: selective serotonin reuptake inhibitor use prior to ICU admission is associated with increased hospital mortality. Chest.

Marzyeh Ghassemi, Jarrad H Van Stan, Daryush D Mehta, Matı́as Zañartu, Harold A Cheyne II, Robert E Hillman, John V Guttag (2014). Learning to detect vocal hyperfunction from ambulatory neck-surface acceleration features: Initial results for vocal fold nodules. IEEE Transactions on Biomedical Engineering.