| A review of causal inference for biomedical informatics S Kleinberg, G Hripcsak Journal of biomedical informatics 44 (6), 1102-1112, 2011 | 213 | 2011 |
| Nonconvulsive seizures after subarachnoid hemorrhage: Multimodal detection and outcomes J Claassen, A Perotte, D Albers, S Kleinberg, JM Schmidt, B Tu, ... Annals of neurology 74 (1), 53-64, 2013 | 211 | 2013 |
| Automated estimation of food type and amount consumed from body-worn audio and motion sensors M Mirtchouk, C Merck, S Kleinberg UbiComp, 2016 | 119 | 2016 |
| Causality, Probability, and Time S Kleinberg Cambridge University Press, 2012 | 109 | 2012 |
| Unintrusive Eating Recognition using Google Glass SA Rahman, C Merck, Y Huang, S Kleinberg Pervasive Health, 2015 | 89 | 2015 |
| The temporal logic of causal structures S Kleinberg, B Mishra Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial …, 2009 | 82 | 2009 |
| Combining Fourier and Lagged k-Nearest Neighbor Imputation for Biomedical Time Series Data SA Rahman, Y Huang, J Claassen, N Heintzman, S Kleinberg Journal of Biomedical Informatics, 2015 | 77 | 2015 |
| Recognizing Eating from Body-Worn Sensors: Combining Free-living and Laboratory Data M Mirtchouk, D Lustig, A Smith, I Ching, M Zheng, S Kleinberg Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2017 | 70 | 2017 |
| Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare C Lu, CK Reddy, P Chakraborty, S Kleinberg, Y Ning IJCAI, 2021 | 58 | 2021 |
| Multimodality Sensing for Eating Recognition C Merck, C Maher, M Mirtchouk, M Zheng, Y Huang, S Kleinberg Pervasive Health, 2016 | 57 | 2016 |
| Automated meal detection from continuous glucose monitor data through simulation and explanation M Zheng, B Ni, S Kleinberg Journal of the American Medical Informatics Association 26 (12), 1592-1599, 2019 | 42 | 2019 |
| A logic for causal inference in time series with discrete and continuous variables S Kleinberg Proceedings of the Twenty-Second international joint conference on …, 2011 | 42 | 2011 |
| How causal information affects decisions M Zheng, JK Marsh, JV Nickerson, S Kleinberg Cognitive Research: Principles and Implications 5, 1-24, 2020 | 38 | 2020 |
| Why: A Guide to Finding and Using Causes S Kleinberg O'Reilly Media, 2015 | 37 | 2015 |
| Artificial intelligence for predicting and diagnosing complications of diabetes J Huang, AM Yeung, DG Armstrong, AN Battarbee, J Cuadros, ... Journal of Diabetes Science and Technology 17 (1), 224-238, 2023 | 32 | 2023 |
| Fast and Accurate Causal Inference from Time Series Data. Y Huang, S Kleinberg FLAIRS Conference, 49-54, 2015 | 23 | 2015 |
| Investigating potentials and pitfalls of knowledge distillation across datasets for blood glucose forecasting H Hameed, S Kleinberg Proceedings of the 5th Annual Workshop on Knowledge Discovery in Healthcare Data, 2020 | 21 | 2020 |
| Using uncertain data from body-worn sensors to gain insight into type 1 diabetes N Heintzman, S Kleinberg Journal of Biomedical Informatics 63, 259-268, 2016 | 20 | 2016 |
| Imputation of Missing Values in Time Series with Lagged Correlations SA Rahman, Y Huang, J Claassen, S Kleinberg 2014 IEEE International Conference on Data Mining Workshop, 753-762, 2014 | 19 | 2014 |
| Machine learning modeling practices to support the principles of AI and ethics in nutrition research DM Thomas, S Kleinberg, AW Brown, M Crow, ND Bastian, N Reisweber, ... Nutr. Diabetes 12, 2022 | 18 | 2022 |
