| Learning to diagnose with LSTM recurrent neural networks ZC Lipton, DC Kale, C Elkan, R Wetzel arXiv preprint arXiv:1511.03677, 2015 | 1294 | 2015 |
| Multitask learning and benchmarking with clinical time series data H Harutyunyan, H Khachatrian, DC Kale, G Ver Steeg, A Galstyan Scientific data 6 (1), 96, 2019 | 813 | 2019 |
| Do no harm: a roadmap for responsible machine learning for health care J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, K Jung, ... Nature medicine 25 (9), 1337-1340, 2019 | 593 | 2019 |
| Deep computational phenotyping Z Che, D Kale, W Li, MT Bahadori, Y Liu Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 313 | 2015 |
| Modeling missing data in clinical time series with rnns ZC Lipton, DC Kale, R Wetzel Machine Learning for Healthcare 56 (56), 253-270, 2016 | 265 | 2016 |
| Directly modeling missing data in sequences with rnns: Improved classification of clinical time series ZC Lipton, D Kale, R Wetzel Machine learning for healthcare conference, 253-270, 2016 | 237 | 2016 |
| Unsupervised pattern discovery in electronic health care data using probabilistic clustering models BM Marlin, DC Kale, RG Khemani, RC Wetzel Proceedings of the 2nd ACM SIGHIT international health informatics symposium …, 2012 | 206 | 2012 |
| Anchored correlation explanation: Topic modeling with minimal domain knowledge RJ Gallagher, K Reing, D Kale, G Ver Steeg Transactions of the Association for Computational Linguistics 5, 529-542, 2017 | 168 | 2017 |
| Learning to diagnose with LSTM recurrent neural networks. arXiv 2015 ZC Lipton, DC Kale, C Elkan, R Wetzel arXiv preprint arXiv:1511.03677, 0 | 59 | |
| The effect of neighborhood and individual characteristics on pediatric critical illness D Epstein, M Reibel, JB Unger, M Cockburn, LA Escobedo, DC Kale, ... Journal of community health 39, 753-759, 2014 | 53 | 2014 |
| Phenotyping of clinical time series with LSTM recurrent neural networks ZC Lipton, DC Kale, RC Wetzel arXiv preprint arXiv:1510.07641, 2015 | 51 | 2015 |
| An examination of multivariate time series hashing with applications to health care DC Kale, D Gong, Z Che, Y Liu, G Medioni, R Wetzel, P Ross 2014 IEEE international conference on data mining, 260-269, 2014 | 47 | 2014 |
| Accelerating active learning with transfer learning D Kale, Y Liu 2013 IEEE 13th International Conference on Data Mining, 1085-1090, 2013 | 43 | 2013 |
| Causal phenotype discovery via deep networks DC Kale, Z Che, MT Bahadori, W Li, Y Liu, R Wetzel AMIA Annual Symposium Proceedings 2015, 677, 2015 | 39 | 2015 |
| Functional subspace clustering with application to time series MT Bahadori, D Kale, Y Fan, Y Liu International conference on machine learning, 228-237, 2015 | 37 | 2015 |
| Hemilaminectomy for thoracolumbar Hansen Type I intervertebral disk disease in ambulatory dogs with or without neurologic deficits: 39 cases (2008–2010) EA Ingram, DC Kale, RJ Balfour Veterinary Surgery 42 (8), 924-931, 2013 | 35 | 2013 |
| Learning effective representations from clinical notes S Dubois, N Romano, DC Kale, N Shah, K Jung stat 1050, 15, 2017 | 28 | 2017 |
| Hierarchical active transfer learning D Kale, M Ghazvininejad, A Ramakrishna, J He, Y Liu Proceedings of the 2015 SIAM International Conference on Data Mining, 514-522, 2015 | 22 | 2015 |
| An examination of deep learning for extreme climate pattern analysis G Iglesias, DC Kale, Y Liu The 5th International Workshop on Climate Informatics, 2015 | 20 | 2015 |
| Sim• TwentyFive: an interactive visualization system for data-driven decision support B Stubbs, DC Kale, A Das AMIA Annual Symposium Proceedings 2012, 891, 2012 | 20 | 2012 |
Zachary C. LiptonProfessor of Machine Learning at Carnegie Mellon; CTO / CSO at AbridgeVerified email at cmu.edu
