Suchi Saria

Research output

Research output per year 2004 2015 2018 2019 2020 2021 2022 2023 2024

• 42 Article
• 9 Conference contribution
• 9 Conference article
• 9 Review article
• 16 More
  • 4 Chapter
  • 4 Paper
  • 4 Comment/debate
  • 2 Editorial
  • 2 Letter

Research output per year

Research output per year

Search results

• 2024

  Large language models for science and medicine

  Telenti, A., Auli, M., Hie, B. L., Maher, C., Saria, S. & Ioannidis, J. P. A., 2024, (Accepted/In press) In: European Journal of Clinical Investigation.

  Research output: Contribution to journal › Review article › peer-review

  • Chemical Compound 100%
  • DNA Sequence 95%
  • Language 73%
  • Medicine 67%
  • Patents 66%
• 2023

  Artificial intelligence model development and validation

  Liu, H., Estiri, H., Wiens, J., Goldenberg, A., Saria, S. & Shah, N., Sep 2 2023, Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril (2019). National Academies Press, p. 131-158 28 p.

  Research output: Chapter in Book/Report/Conference proceeding › Chapter

• Biological research and self-driving labs in deep space supported by artificial intelligence

  Sanders, L. M., Scott, R. T., Yang, J. H., Qutub, A. A., Garcia Martin, H., Berrios, D. C., Hastings, J. J. A., Rask, J., Mackintosh, G., Hoarfrost, A. L., Chalk, S., Kalantari, J., Khezeli, K., Antonsen, E. L., Babdor, J., Barker, R., Baranzini, S. E., Beheshti, A., Delgado-Aparicio, G. M., Glicksberg, B. S., & 36 othersGreene, C. S., Haendel, M., Hamid, A. A., Heller, P., Jamieson, D., Jarvis, K. J., Komarova, S. V., Komorowski, M., Kothiyal, P., Mahabal, A., Manor, U., Mason, C. E., Matar, M., Mias, G. I., Miller, J., Myers, J. G., Nelson, C., Oribello, J., Park, S. M., Parsons-Wingerter, P., Prabhu, R. K., Reynolds, R. J., Saravia-Butler, A., Saria, S., Sawyer, A., Singh, N. K., Snyder, M., Soboczenski, F., Soman, K., Theriot, C. A., Van Valen, D., Venkateswaran, K., Warren, L., Worthey, L., Zitnik, M. & Costes, S. V., Mar 2023, In: Nature Machine Intelligence. 5, 3, p. 208-219 12 p.

  Research output: Contribution to journal › Review article › peer-review

  Open Access

  • Artificial intelligence 100%
  • Crops 42%
  • Ecosystems 40%
  • Experiments 40%
  • Metadata 37%

• Biomonitoring and precision health in deep space supported by artificial intelligence

  Scott, R. T., Sanders, L. M., Antonsen, E. L., Hastings, J. J. A., Park, S. M., Mackintosh, G., Reynolds, R. J., Hoarfrost, A. L., Sawyer, A., Greene, C. S., Glicksberg, B. S., Theriot, C. A., Berrios, D. C., Miller, J., Babdor, J., Barker, R., Baranzini, S. E., Beheshti, A., Chalk, S., Delgado-Aparicio, G. M., & 36 othersHaendel, M., Hamid, A. A., Heller, P., Jamieson, D., Jarvis, K. J., Kalantari, J., Khezeli, K., Komarova, S. V., Komorowski, M., Kothiyal, P., Mahabal, A., Manor, U., Garcia Martin, H., Mason, C. E., Matar, M., Mias, G. I., Myers, J. G., Nelson, C., Oribello, J., Parsons-Wingerter, P., Prabhu, R. K., Qutub, A. A., Rask, J., Saravia-Butler, A., Saria, S., Singh, N. K., Snyder, M., Soboczenski, F., Soman, K., Van Valen, D., Venkateswaran, K., Warren, L., Worthey, L., Yang, J. H., Zitnik, M. & Costes, S. V., Mar 2023, In: Nature Machine Intelligence. 5, 3, p. 196-207 12 p.

  Research output: Contribution to journal › Review article › peer-review

  Open Access

  • Artificial intelligence 100%
  • Health 83%
  • Earth (planet) 63%
  • Health hazards 53%
  • Biomarkers 47%

• Birds of an Odd Feather: Guaranteed Out-of-Distribution (OOD) Novel Category Detection

  Wald, Y. & Saria, S., 2023, In: Proceedings of Machine Learning Research. 216, p. 2179-2191 13 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Birds 100%
  • Machine learning 63%
  • Odd 61%
  • Rare Events 21%
  • Healthcare 21%

• Deploying artificial intelligence in clinical settings

  Fihn, S., Saria, S., Mendonça, E., Hain, S., Matheny, M., Shah, N., Liu, H. & Auerbach, A., Sep 2 2023, Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril (2019). National Academies Press, p. 159-196 38 p.

  Research output: Chapter in Book/Report/Conference proceeding › Chapter

• Efficient Approximate Predictive Inference Under Feedback Covariate Shift with Influence Functions

  Prinster, D., Saria, S. & Liu, A., 2023, In: Proceedings of Machine Learning Research. 204, p. 324-327 4 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Predictive Inference 100%
  • Influence Function 82%
  • Jackknife 80%
  • Computational Efficiency 72%
  • Protein 70%

• JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift

  Prinster, D., Saria, S. & Liu, A., 2023, In: Proceedings of Machine Learning Research. 202, p. 28167-28190 24 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Covariates 100%
  • Prediction 94%
  • Feedback 74%
  • Standards 65%
  • Jackknife 40%

• Potential trade-offs and unintended consequences of artificial intelligence

  Chen, J., Beam, A., Saria, S. & Mendonça, E. A., Sep 2 2023, Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril (2019). National Academies Press, p. 99-130 32 p.

  Research output: Chapter in Book/Report/Conference proceeding › Chapter

• The promise of AI and technology to improve quality of life and care for older adults

  Directors & Advisors of the AITCs, Jun 2023, In: Nature Aging. 3, 6, p. 629-631 3 p.

  Research output: Contribution to journal › Letter › peer-review

  Open Access
• 2022

  A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models

  Wang, H. E., Landers, M., Adams, R., Subbaswamy, A., Kharrazi, H., Gaskin, D. J. & Saria, S., Aug 1 2022, In: Journal of the American Medical Informatics Association. 29, 8, p. 1323-1333 11 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Patient Readmission 100%
  • Checklist 84%
  • Health Personnel 45%
  • Healthcare Disparities 24%
  • Uncertainty 15%

• A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy

  Bigelow, E., Saria, S., Piening, B., Curti, B., Dowdell, A., Weerasinghe, R., Bifulco, C., Urba, W., Finkelstein, N., Fertig, E. J., Baras, A., Zaidi, N., Jaffee, E. & Yarchoan, M., Jan 1 2022, In: Cancer Informatics. 21

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Forests 100%
  • Tumor Burden 80%
  • Immunotherapy 74%
  • Cell Death 63%
  • Machine Learning 35%

• A unifying causal framework for analyzing dataset shift-stable learning algorithms

  Subbaswamy, A., Chen, B. & Saria, S., Jan 1 2022, In: Journal of Causal Inference. 10, 1, p. 64-89 26 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Stable Distribution 100%
  • Learning Algorithm 98%
  • Minimax 78%
  • Graphics 68%
  • Hierarchy 67%

• Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing

  Henry, K. E., Adams, R., Parent, C., Soleimani, H., Sridharan, A., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., Klein, E. Y., Chen, E. S., Saheed, M. O., Henley, M., Miranda, S., Houston, K., Linton, R. C., Ahluwalia, A. R., Wu, A. W. & Saria, S., Jul 2022, In: Nature medicine. 28, 7, p. 1447-1454 8 p.

  Research output: Contribution to journal › Article › peer-review

  • Machine Learning 100%
  • Sepsis 71%
  • Time 31%
  • Antimicrobial Agent 30%
  • Reduction 20%

• Human–machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system

  Henry, K. E., Kornfield, R., Sridharan, A., Linton, R. C., Groh, C., Wang, T., Wu, A., Mutlu, B. & Saria, S., Dec 2022, In: npj Digital Medicine. 5, 1, 97.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Machine Learning 100%
  • Learning systems 89%
  • Machine learning 64%
  • Workflow 24%
  • Sepsis 17%

• JAWS: Auditing Predictive Uncertainty Under Covariate Shift

  Prinster, D., Liu, A. & Saria, S., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Uncertainty 100%
  • Labels 45%

• Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology

  Javaid, A., Zghyer, F., Kim, C., Spaulding, E. M., Isakadze, N., Ding, J., Kargillis, D., Gao, Y., Rahman, F., Brown, D. E., Saria, S., Martin, S. S., Kramer, C. M., Blumenthal, R. S. & Marvel, F. A., Dec 2022, In: American Journal of Preventive Cardiology. 12, 100379.

  Research output: Contribution to journal › Review article › peer-review

  Open Access

  • Biomedical Technology 100%
  • Machine Learning 88%
  • Medicine 57%
  • Cardiovascular Diseases 57%
  • Health Equity 36%

• Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis

  Adams, R., Henry, K. E., Sridharan, A., Soleimani, H., Zhan, A., Rawat, N., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., Klein, E. Y., Chen, E. S., Saheed, M. O., Henley, M., Miranda, S., Houston, K., Linton, R. C., Ahluwalia, A. R., Wu, A. W. & Saria, S., Jul 2022, In: Nature medicine. 28, 7, p. 1455-1460 6 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Machine Learning 100%
  • Sepsis 71%
  • Health Care Outcome Assessment 58%
  • Reduction 40%
  • Time 21%

• Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (Nature Medicine, (2022), 28, 5, (924-933), 10.1038/s41591-022-01772-9)

  the DECIDE-AI expert group, Oct 2022, In: Nature medicine. 28, 10, p. 2218 1 p.

  Research output: Contribution to journal › Comment/debate › peer-review

  Open Access

  • Artificial Intelligence 100%
  • Clinical Decision Support Systems 98%
  • Medicine 55%
  • Guidelines 48%

• Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

  Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., Liu, X., Mateen, B. A., Mathur, P., Mccradden, M. D., Morgan, L., Ordish, J., Rogers, C., Saria, S., Ting, D. S. W., Watkinson, P., & 3 othersWeber, W., Wheatstone, P. & Mcculloch, P., 2022, (Accepted/In press) In: The BMJ. e070904.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Artificial Intelligence 100%
  • Clinical Decision Support Systems 98%
  • Guidelines 48%
  • Checklist 16%
  • Evaluation Studies 15%

• Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

  the DECIDE-AI expert group, May 2022, In: Nature medicine. 28, 5, p. 924-933 10 p.

  Research output: Contribution to journal › Review article › peer-review

  Open Access

  • Artificial Intelligence 100%
  • Clinical Decision Support Systems 98%
  • Developmental 91%
  • Safety 64%
  • Guidelines 48%

• Research gaps and opportunities in precision nutrition: an NIH workshop report

  Lee, B. Y., Ordovás, J. M., Parks, E. J., Anderson, C. A. M., Barabási, A. L., Clinton, S. K., de la Haye, K., Duffy, V. B., Franks, P. W., Ginexi, E. M., Hammond, K. J., Hanlon, E. C., Hittle, M., Ho, E., Horn, A. L., Isaacson, R. S., Mabry, P. L., Malone, S., Martin, C. K., Mattei, J., & 15 othersMeydani, S. N., Nelson, L. M., Neuhouser, M. L., Parent, B., Pronk, N. P., Roche, H. M., Saria, S., Scheer, F. A. J. L., Segal, E., Sevick, M. A., Spector, T. D., Van Horn, L., Varady, K. A., Voruganti, V. S. & Martinez, M. F., Dec 19 2022, In: The American journal of clinical nutrition. 116, 6, p. 1877-1900 24 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Environmental Exposure 100%
  • Workforce 89%
  • Education 87%
  • Health 67%
  • Dietary Exposure 62%

• Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients With Acute Heart Failure

  Rahman, F., Finkelstein, N., Alyakin, A., Gilotra, N. A., Trost, J., Schulman, S. P. & Saria, S., May 1 2022, In: Journal of the Society for Cardiovascular Angiography and Interventions. 1, 3, 100308.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Cardiogenic Shock 100%
  • Machine Learning 96%
  • Heart Failure 62%
  • Vital Signs 7%
  • Cardiomyopathies 6%
• 2021

  Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients

  Mathioudakis, N. N., Abusamaan, M. S., Shakarchi, A. F., Sokolinsky, S., Fayzullin, S., McGready, J., Zilbermint, M., Saria, S., Golden, S. H. & Mathioudakis, N. N., Jan 2021, In: JAMA Network Open. 4, 1, e2030913.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Machine Learning 100%
  • Hypoglycemia 85%
  • Blood Glucose 62%
  • Hospitalization 37%
  • Inpatients 30%

• Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients

  Saria, S., Schulam, P., Yeh, B. J., Burke, D., Mooney, S. D., Fong, C. T., Sunshine, J. E., Long, D. R. & O'reilly-Shah, V. N., Jun 4 2021, In: Critical Care Explorations. 3, 6, p. E0441

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • AIDS-Related Complex 100%
  • Coronavirus 90%
  • Respiratory Insufficiency 67%
  • Patient Admission 9%
  • Electronic Health Records 9%

• Development and validation of high definition phenotype-based mortality prediction in critical care units

  Sun, Y., Kaur, R., Gupta, S., Paul, R., Das, R., Cho, S. J., Anand, S., Boutilier, J. J., Saria, S., Palma, J., Saluja, S., McAdams, R. M., Kaur, A., Yadav, G. & Singh, H., 2021, In: JAMIA Open. 4, 1, p. 1-13 13 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Critical Care 100%
  • Logistic Models 74%
  • Phenotype 68%
  • Mortality 57%
  • Memory 41%

• Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption

  Landers, M., Dorsey, R. & Saria, S., Sep 13 2021, In: Digital Biomarkers. 5, 3, p. 216-223 8 p.

  Research output: Contribution to journal › Review article › peer-review

  Open Access

  • Health 100%
  • Health care 92%
  • Remote patient monitoring 72%
  • Coronavirus 71%
  • Sensors 69%

• Evaluating Model Robustness and Stability to Dataset Shift

  Subbaswamy, A., Adams, R. & Saria, S., 2021, In: Proceedings of Machine Learning Research. 130, p. 2611-2619 9 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Model Robustness 100%
  • Safety 35%
  • Machine Learning 34%
  • Machine learning 30%
  • Data Distribution 19%

• Machine learning-based automatic classification of video recorded neonatal manipulations and associated physiological parameters: A feasibility study

  Singh, H., Kusuda, S., McAdams, R. M., Gupta, S., Kalra, J., Kaur, R., Das, R., Anand, S., Pandey, A. K., Cho, S. J., Saluja, S., Boutilier, J. J., Saria, S., Palma, J., Kaur, A., Yadav, G. & Sun, Y., Jan 2021, In: Children. 8, 1, 1.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Machine Learning 100%
  • Feasibility Studies 92%
  • Neonatal Intensive Care Units 71%
  • Heart Rate 48%
  • Enteral Nutrition 47%

• Partial Identifiability in Discrete Data with Measurement Error

  Finkelstein, N., Adams, R., Saria, S. & Shpitser, I., 2021, p. 1798-1808. 11 p.

  Research output: Contribution to conference › Paper › peer-review

  • Partial Identification 100%
  • Discrete Data 97%
  • Identifiability 90%
  • Measurement Error 86%
  • Measurement errors 83%

• Partial Identifiability in Discrete Data With Measurement Error

  Finkelstein, N., Adams, R., Saria, S. & Shpitser, I., 2021, In: Proceedings of Machine Learning Research. 161, p. 1798-1808 11 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Partial Identification 100%
  • Discrete Data 97%
  • Identifiability 90%
  • Measurement Error 86%
  • Measurement errors 83%

• Predictors of the start of declining eGFR in patients with systemic lupus erythematosus

  Yip, T. C. F., Saria, S., Petri, M. & Magder, L. S., Jan 2021, In: Lupus. 30, 1, p. 15-24 10 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Glomerular Filtration Rate 100%
  • Systemic Lupus Erythematosus 99%
  • Prednisone 23%
  • Hematocrit 23%
  • African Americans 21%

• The clinician and dataset shift in artificial intelligence

  Finlayson, S. G., Subbaswamy, A., Singh, K., Bowers, J., Kupke, A., Zittrain, J., Kohane, I. S. & Saria, S., Jul 15 2021, In: New England Journal of Medicine. 385, 3, p. 283-286 4 p.

  Research output: Contribution to journal › Letter › peer-review

  Open Access

• Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson's Disease?

  Landers, M., Saria, S. & Espay, A. J., 2021, In: Journal of Parkinson's Disease. 11, s1, p. S117-S122

  Research output: Contribution to journal › Review article › peer-review

  Open Access

  • Artificial Intelligence 100%
  • Movement Disorders 84%
  • Parkinson Disease 63%
  • Precision Medicine 27%
  • Datasets 19%
• 2020

  An Individualized, Data-Driven Digital Approach for Precision Behavior Change

  Wongvibulsin, S., Martin, S. S., Saria, S., Zeger, S. L. & Murphy, S. A., May 1 2020, In: American Journal of Lifestyle Medicine. 14, 3, p. 289-293 5 p.

  Research output: Contribution to journal › Review article › peer-review

  Open Access

  • Chronic Diseases 100%
  • Chronic Disease 65%
  • health behavior 36%
  • Health Behavior 36%
  • Technology 23%
  3 Scopus citations

• Can you trust this prediction? Auditing pointwise reliability after learning

  Schulam, P. & Saria, S., 2020.

  Research output: Contribution to conference › Paper › peer-review

  • Uncertainty Estimation 100%
  • Auditing 93%
  • Resampling 66%
  • Learning 54%
  • Prediction 47%
  5 Scopus citations

• Deep Phenotyping of Parkinson's Disease

  Dorsey, E. R., Omberg, L., Waddell, E., Adams, J. L., Adams, R., Ali, M. R., Amodeo, K., Arky, A., Augustine, E. F., DInesh, K., Hoque, M. E., Glidden, A. M., Jensen-Roberts, S., Kabelac, Z., Katabi, DI., Kieburtz, K., Kinel, D. R., Little, M. A., Lizarraga, K. J., Myers, T., & 11 othersRiggare, S., Rosero, S. Z., Saria, S., Schifitto, G., Schneider, R. B., Sharma, G., Shoulson, I., Stevenson, E. A., Tarolli, C. G., Luo, J. & McDermott, M. P., 2020, In: Journal of Parkinson's Disease. 10, 3, p. 855-873 19 p.

  Research output: Contribution to journal › Review article › peer-review

  Open Access

  • Parkinson Disease 100%
  • Phenotype 43%
  • Physical Examination 20%
  • Molecular Biology 19%
  • Patient Care 18%
  6 Scopus citations

• From development to deployment: dataset shift, causality, and shift-stable models in health AI

  Subbaswamy, A. & Saria, S., Apr 1 2020, In: Biostatistics (Oxford, England). 21, 2, p. 345-352 8 p.

  Research output: Contribution to journal › Article › peer-review
  14 Scopus citations

• Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist

  Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I. S., Saria, S., Topol, E., Obermeyer, Z., Yu, B. & Butte, A. J., Sep 1 2020, In: Nature medicine. 26, 9, p. 1320-1324 5 p.

  Research output: Contribution to journal › Comment/debate › peer-review
  18 Scopus citations

• Preventing failures due to dataset shift: Learning predictive models that transport

  Subbaswamy, A., Schulam, P. & Saria, S., 2020.

  Research output: Contribution to conference › Paper › peer-review

  • Predictive Model 100%
  • Surgery 73%
  • Learning 66%
  • Target 62%
  • Training 51%
  5 Scopus citations

• Reporting and implementing interventions involving machine learning and artificial intelligence

  Bates, D. W., Auerbach, A., Schulam, P., Wright, A. & Saria, S., Jun 2 2020, In: Annals of internal medicine. 172, 11, p. S137-S144

  Research output: Contribution to journal › Article › peer-review

  • Artificial Intelligence 100%
  • Machine Learning 85%
  • Delivery of Health Care 19%
  • Clinical Decision Support Systems 14%
  • Electronic Health Records 11%
  13 Scopus citations

• The Association Between Neighborhood Socioeconomic Disadvantage and Readmissions for Patients Hospitalized With Sepsis

  Galiatsatos, P., Follin, A., Alghanim, F., Sherry, M., Sylvester, C., Daniel, Y., Chanmugam, A., Townsend, J., Saria, S., Kind, A. J. & Chen, E., Jun 1 2020, In: Critical care medicine. 48, 6, p. 808-814 7 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Patient Readmission 100%
  • Sepsis 69%
  • Logistic Models 25%
  • Vulnerable Populations 20%
  • International Classification of Diseases 19%
  2 Scopus citations

• Too Many Definitions of Sepsis: Can Machine Learning Leverage the Electronic Health Record to Increase Accuracy and Bring Consensus?

  Saria, S. & Henry, K. E., Feb 1 2020, In: Critical care medicine. 48, 2, p. 137-141 5 p.

  Research output: Contribution to journal › Editorial › peer-review
  5 Scopus citations
• 2019

  Active learning for decision-making from imbalanced observational data

  Sundin, I., Schulam, P., Siivola, E., Vehtari, A., Saria, S. & Kaski, S., Jan 1 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 10578-10587 10 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Problem-Based Learning 100%
  • Machine learning 63%
  • Decision making 62%
  • decision making 54%
  • learning 29%

• Author Correction: Do no harm: a roadmap for responsible machine learning for health care (Nature Medicine, (2019), 25, 9, (1337-1340), 10.1038/s41591-019-0548-6)

  Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S. & Goldenberg, A., Oct 1 2019, In: Nature medicine. 25, 10, p. 1627 1 p.

  Research output: Contribution to journal › Comment/debate › peer-review

  Open Access

  • Machine Learning 100%
  • Ontario 94%
  • Child Development 91%
  • Canada 80%
  • Error 66%
  1 Scopus citations

• Comparison of automated activity recognition to provider observations of patient mobility in the ICU

  Rawat, N., Rao, V., Peven, M., Shrock, C., Reiter, A., Saria, S. & Ali, H., 2019, In: Critical care medicine. 47, 9, p. 1232-1234 3 p.

  Research output: Contribution to journal › Article › peer-review

  • Surgical Instruments 100%
  • Datasets 81%
  • Documentation 48%
  • Caregivers 44%
  • Observational Studies 40%
  1 Scopus citations

• Comparison of Automated Sepsis Identification Methods and Electronic Health Record-based Sepsis Phenotyping: Improving Case Identification Accuracy by Accounting for Confounding Comorbid Conditions

  Henry, K. E., Hager, D. N., Osborn, T. M., Wu, A. W. & Saria, S., Oct 16 2019, In: Critical Care Explorations. 1, 10, p. E0053

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Electronic Health Records 100%
  • Sepsis 78%
  • Hospital Emergency Service 18%
  • Inpatients 9%
  • Centers for Medicare and Medicaid Services (U.S.) 7%

• Do no harm: a roadmap for responsible machine learning for health care

  Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S. & Goldenberg, A., Sep 1 2019, In: Nature medicine. 25, 9, p. 1337-1340 4 p.

  Research output: Contribution to journal › Article › peer-review

  • Machine Learning 100%
  • Delivery of Health Care 53%
  • Research Design 35%
  • Patient Care 34%
  • Medicine 32%
  87 Scopus citations

• Learning models from data with measurement error: Tackling underreporting

  Adams, R., Ji, Y., Wang, X. & Saria, S., Jan 1 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 95-104 10 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Measurement errors 100%
  • Public health 57%
  • smoking 45%
  • Sensitivity analysis 42%
  • learning 33%
  1 Scopus citations

• Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

  Badawy, R., Hameed, F., Bataille, L., Little, M. A., Claes, K., Saria, S., Cedarbaum, J. M., Stephenson, D., Neville, J., Maetzler, W., Espay, A. J., Bloem, B. R., Simuni, T. & Karlin, D. R., Sep 1 2019, In: Digital Biomarkers. 3, 3, p. 116-132 17 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Metadata 100%
  • Clinical research 94%
  • Biomedical Technology 86%
  • Health 49%
  • Data Management 12%