Papers

(2021). Challenges of Differentially Private Prediction in Healthcare Settings.

(2021). What every reader should know about studies using electronic health record data but may be afraid to ask. Journal of medical Internet research.

(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.

(2021). The role of machine learning in clinical research: transforming the future of evidence generation. Trials.

(2021). Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. International Conference on Machine Learning.

(2021). Reproducibility in machine learning for health research: Still a ways to go. Science Translational Medicine.

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

(2021). Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing. arXiv preprint arXiv:2108.12510.

(2021). Problems in the deployment of machine-learned models in health care. CMAJ.

(2021). Predicting hospitalizations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study. medRxiv.

(2021). Medical imaging algorithms exacerbate biases in underdiagnosis.

(2021). Implementing machine learning in medicine. CMAJ.

(2021). Five principles for the intelligent use of AI in medical imaging. Springer.

(2021). Equity in essence: a call for operationalising fairness in machine learning for healthcare. BMJ Health & Care Informatics.

(2021). Do as AI say: susceptibility in deployment of clinical decision-aids. NPJ digital medicine.

(2021). Dear Watch, Should I Get a COVID-19 Test? Designing deployable machine learning for wearables. medRxiv.

(2021). Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.

(2021). Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. arXiv preprint arXiv:2107.09562.

(2021). Characteristics and outcomes of hospital admissions for COVID-19 and influenza in the Toronto area. CMAJ.

(2021). Caractéristiques et issues des hospitalisations pour les cas de COVID-19 et d’influenza dans la région de Toronto. CMAJ.

(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.

(2021). An empirical framework for domain generalization in clinical settings. Proceedings of the Conference on Health, Inference, and Learning.

(2021). A comparison of approaches to improve worst-case predictive model performance over patient subpopulations. arXiv preprint arXiv:2108.12250.

(2020). Uniform Priors for Data-Efficient Transfer. arXiv preprint arXiv:2006.16524.

(2020). Treating health disparities with artificial intelligence. Nature medicine.

(2020). Ssmba: Self-supervised manifold based data augmentation for improving out-of-domain robustness. arXiv preprint arXiv:2009.10195.

(2020). Self-supervised contrastive learning of protein representations by mutual information maximization. BioRxiv.

(2020). S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. arXiv preprint arXiv:2009.08348.

(2020). Probabilistic machine learning for healthcare. Annual Review of Biomedical Data Science.

(2020). Preparing a Clinical Support Model for Silent Mode in General Internal Medicine. Machine Learning for Healthcare Conference.

(2020). Predicting covid-19 pneumonia severity on chest x-ray with deep learning. Cureus.

(2020). Patient characteristics, clinical care, resource use, and outcomes associated with hospitalization for COVID-19 in the Toronto area. medRxiv.

(2020). Multiple sclerosis severity classification from clinical text. arXiv preprint arXiv:2010.15316.

(2020). Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii. Proceedings of the ACM Conference on Health, Inference, and Learning.

(2020). Improving Dialogue Breakdown Detection with Semi-Supervised Learning. arXiv preprint arXiv:2011.00136.

(2020). Hurtful words: quantifying biases in clinical contextual word embeddings. proceedings of the ACM Conference on Health, Inference, and Learning.

(2020). Ethical Machine Learning in Healthcare. Annual Review of Biomedical Data Science.

(2020). Ensuring machine learning for healthcare works for all. BMJ Health & Care Informatics.

(2020). Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation. Machine Learning for Health Workshop.

(2020). Covid-19 image data collection: Prospective predictions are the future. arXiv preprint arXiv:2006.11988.

(2020). Counterfactually guided policy transfer in clinical settings. arXiv preprint arXiv:2006.11654.

(2020). Confounding Feature Acquisition for Causal Effect Estimation. Machine Learning for Health.

(2020). Chexpert++: Approximating the chexpert labeler for speed, differentiability, and probabilistic output. Machine Learning for Healthcare Conference.

(2020). CheXclusion: Fairness gaps in deep chest X-ray classifiers. BIOCOMPUTING 2021: Proceedings of the Pacific Symposium.

(2020). Challenges to the reproducibility of machine learning models in health care. Jama.

(2020). Building a data science platform for better health.

(2020). An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare. arXiv preprint arXiv:2011.11235.

(2020). A review of challenges and opportunities in machine learning for health. AMIA Summits on Translational Science Proceedings.

(2020). A comprehensive evaluation of multi-task learning and multi-task pre-training on EHR time-series data. arXiv preprint arXiv:2007.10185.

(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.

(2019). Turning the crank for machine learning: ease, at what expense?. The Lancet Digital Health.

(2019). Towards characterizing the high-dimensional bias of kernel-based particle inference algorithms.

(2019). The PLOS ONE collection on machine learning in health and biomedicine: Towards open code and open data. PloS one.

(2019). The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers. arXiv preprint arXiv:1906.07282.

(2019). Reproducibility in machine learning for health. arXiv preprint arXiv:1907.01463.

(2019). Practical guidance on artificial intelligence for health-care data. The Lancet Digital Health.

(2019). Modeling the biological pathology continuum with hsic-regularized wasserstein auto-encoders. arXiv preprint arXiv:1901.06618.

(2019). Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks. Machine Learning for Healthcare Conference.

(2019). Do no harm: a roadmap for responsible machine learning for health care. Nature medicine.

(2019). Clinically accurate chest x-ray report generation. Machine Learning for Healthcare Conference.

(2019). Can AI help reduce disparities in general medical and mental health care?. AMA journal of ethics.

(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.

(2018). The effect of heterogeneous data for Alzheimer's disease detection from speech. arXiv preprint arXiv:1811.12254.

(2018). Semi-supervised biomedical translation with cycle wasserstein regression GANs. Proceedings of the AAAI Conference on Artificial Intelligence.

(2018). Rethinking clinical prediction: why machine learning must consider year of care and feature aggregation. arXiv preprint arXiv:1811.12583.

(2018). Racial disparities and mistrust in end-of-life care. Machine Learning for Healthcare Conference.

(2018). Opportunities in machine learning for healthcare. arXiv preprint arXiv:1806.00388.

(2018). Modeling mistrust in end-of-life care. arXiv preprint arXiv:1807.00124.

(2018). Machine learning for health (ML4H) workshop at NeurIPS 2018. arXiv preprint arXiv:1811.07216.

(2018). Clinicalvis: Supporting clinical task-focused design evaluation. arXiv preprint arXiv:1810.05798.

(2018). Ambulatory assessment of phonotraumatic vocal hyperfunction using glottal airflow measures estimated from neck-surface acceleration. PLoS One.

(2017). Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database. Journal of the American Medical Informatics Association.

(2017). The use of autoencoders for discovering patient phenotypes. arXiv preprint arXiv:1703.07004.

(2017). Predicting intervention onset in the ICU with switching state space models. AMIA Summits on Translational Science Proceedings.

(2017). Learning Clinical Events.

(2017). Deep reinforcement learning for sepsis treatment. arXiv preprint arXiv:1711.09602.

(2017). Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach. Machine Learning for Healthcare Conference.

(2017). Clinical intervention prediction and understanding using deep networks. arXiv preprint arXiv:1705.08498.

(2016). Uncovering voice misuse using symbolic mismatch. Machine Learning for Healthcare Conference.

(2016). Sodium modelling to reduce intradialytic hypotension during haemodialysis for acute kidney injury in the intensive care unit. Nephrology.

(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).

(2016). Predicting early psychiatric readmission with natural language processing of narrative discharge summaries. Translational psychiatry.

(2016). Data Analysis. Secondary Analysis of Electronic Health Records.

(2015). Using ambulatory voice monitoring to investigate common voice disorders: Research update. Frontiers in bioengineering and biotechnology.

(2015). State of the art review: the data revolution in critical care. Critical Care.

(2015). Short-term mortality prediction for elderly patients using Medicare claims data. International journal of machine learning and computing.

(2015). Metadata correction: making big data useful for health care: a summary of the inaugural mit critical data conference. JMIR medical informatics.

(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.

(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). Subglottal ambulatory monitoring of vocal function to improve voice disorder assessment. The Journal of the Acoustical Society of America.

(2014). Making big data useful for health care: a summary of the inaugural mit critical data conference. JMIR medical informatics.

(2014). Leveraging a critical care database: selective serotonin reuptake inhibitor use prior to ICU admission is associated with increased hospital mortality. Chest.

(2014). Learning to detect vocal hyperfunction from ambulatory neck-surface acceleration features: Initial results for vocal fold nodules. IEEE Transactions on Biomedical Engineering.