D. Ding, C. Simpson, S. Pfohl, D. Kale, K. Jung, and N. Shah. The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data. arXiv. H. Harutyunyan, H. Khachatrian, D. Kale, G. Ver Steeg, and A. Galstyan. Multitask Learning and Benchmarking with Clinical Time Series Data. arXiv. Code and data. S. Dubois, N. Romano, D. Kale, N. Shah, and K. Jung. Effective Representations from Clinical Notes. arXiv.
H. Harutyunyan, H. Khachatrian, D. Kale, G. Ver Steeg, and A. Galstyan. A Public Benchmark for Clinical Prediction and Multitask Learning. NIPS 2017 Workshop on Machine Learning for Health Spotlight talk. Spotlight slide. Poster. Code and data.
T. Quisel, L. Foschini, A. Signorini, and D. Kale. Collecting and Analyzing Millions of mHealth Data Streams. Proceedings of the 23rd ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2017. PDF.
S. Dubois, N. Romano, D. Kale, N. Shah, and K. Jung. The Effectiveness of Transfer Learning in Electronic Health Records. International Conference on Learning Representations Workshop Track, 2017. PDF. Reviews. T. Quisel, D. Kale, and L. Foschini. Intra-day Activity Better Predicts Chronic Conditions. NIPS 2016 Workshop on Machine Learning for Healthcare (NIPS ML4HC), 2016. arXiv.
Z. Lipton,* D. Kale,* and R. Wetzel. Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series. To appear in the proceedings of the Machine Learning for Health Care (MLCH) Conference, 2016. Proceedings. arXiv.
K. Reing, D. Kale, G. Ver Steeg, and A. Galstyan. Toward Interpretable Topic Discovery via Anchored Correlation Explanation. ICML Workshop on Human Interpretability in Machine Learning (WHI), 2016. arXiv. Code.
Z. Lipton,* D. Kale,* C. Elkan, and R. Wetzel. Learning to Diagnose with LSTM Recurrent Neural Networks. International Conference on Learning Representations, 2016. arXiv. Code.
Z. Lipton,* D. Kale,* and R. Wetzel. Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks. NIPS 2015 Workshop on Machine Learning in Healthcare, 2015. Abstract. arXiv.
D. Kale, Z. Che, M. Taha Bahadori, Wenzhe Li, and Y. Liu. Causal Phenotype Discovery via Deep Networks. Proceedings of the American Medical Informatics Assocation (AMIA) 2015 Annual Symposium, 2015. Paper. Slides.
G. Iglesias, D. Kale, and Y. Liu. An Examination of Deep Learning for Extreme Climate Pattern Analysis. 5th International Workshop on Climate Informatics Proceedings. Online.
Z. Che,* D. Kale,* Wenzhe Li, M. Taha Bahadori, and Y. Liu. Deep Computational Phenotyping. Proceedings of the 21st ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2015. Paper. Slides. Online.
M. Taha Bahadori, D. Kale, Y. Fan, and Y. Liu. Functional Subspace Clustering with Application to Time Series. Proceedings of the 2015 International Conference on Machine Learning (ICML), 2015. Paper.
D. Kale, M. Ghazvininejad, A. Ramakrishna, J. He, and Y. Liu. Hierarchical Active Transfer Learning. Proceedings of the 2015 SIAM International Conference on Data Mining (SDM), 2015. Paper. Slides. Github.
D. Kale,* D. Gong,* Z. Che,* Yan Liu, G. Medioni, R. Wetzel, and P. Ross. An Examination of Multivariate Time Series Hashing with Applications to Health Care. Proceedings of the IEEE 14th International Conference on Data Mining (ICDM), 2014. Paper. Online.
D. Kale, Z. Che, Y. Liu, and R. Wetzel. Computational discovery of physiomes in critically ill children using deep learning. 1st Workshop on Data Mining for Medical Informatics (DMMI), American Medical Informatics Assocation (AMIA), 2014. Abstract. Slides.
D. Kale and Y. Liu. Accelerating Active Learning with Transfer Learning. Proceedings of the IEEE 13th International Conference on Data Mining (ICDM), 2013. Paper. Slides. Online. Github.
E. B. Celikkaya, C. Shelton, D. Kale, R. Wetzel, and R. Khemani. Non-invasive Blood Gas Estimation for Pediatric Mechanical Ventilation. NIPS 2013 Workshop on Machine Learning for Clinical Data Analysis and Healthcare.
D. Kale, S. Di, Y. Liu, and Y. Gil. Capturing Data Analytics Expertise with Visualization in Workflows. AAAI Fall Symposium Series Discovery Informatics Workshop (DIS), 2013.
B. Stubbs and D. Kale. Sim*TwentyFive: An Interactive Visualization System for Data-Driven Decision Support. Proceedings of the American Medical Informatics Association Annual Symposium (AMIA), 2012. Paper. Slides. Online. Demo.
B. Marlin, D. Kale, R. Khemani, and R. Wetzel. Unsupervised Pattern Discovery in Electronic Health Care Data Using Probabilistic Clustering Models. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (IHI): pg. 389-98, 2012. Paper. Online.
D. Crichton, C. Mattmann, A. Hart, D. Kale, R. Khemani, P. Ross, S. Rubin, P. Veeravatanayothin, A. Braverman, C. Goodale, and R. Wetzel. An Informatics Architecture for the Virtual Pediatric Intensive Care Unit. In proceedings of the 24th IEEE International Symposium on Computer-Based Medical Systems (CBMS), pages 1-6, 2011. Online.
D. Kale, A. Hart, C. Mattmann, R. Khemani, P. Ross, P. Vee, J. Terry, R. Wetzel, and D. Crichton. An Open Source, Grid-based Software Framework for Management and Sharing of Pediatric ICU Data. In proceedings of the 9th International Conference on Complexity in Acute Illness (ICCAI), 2010.
D. Epstein, M. Reibel, J. B. Unger, M. Cockburn, L. A. Escobedo, D. Kale, J. C. Chang, and J. I. Gold. The Effect of Neighborhood and Individual Characteristics on Pediatric Critical Illness. Journal of Community Health: Feb. 2014. Online.
R. G. Khemani, E. B. Celikkaya, C. R. Shelton, D. Kale, P. A. Ross, R. C. Wetzel, and C. J. L. Newth. Algorithms to estimate PaCO2 and pH using non invasive parameters for children with Hypoxemic Respiratory Failure. In Respiratory Care, Dec. 2013. Online.
E. Ingram, D. Kale, and R. Balfour. Hemilaminectomy for thoracolumbar Hansen Type I intervertebral disk disease in ambulatory dogs with or without neurologic deficits. Journal of Veterinary Surgery: v. 42, #8, pg. 924-931, Nov. 2013. Online.
D. Kale and D. Stork. Estimating the Position of Illuminants in Paintings Under Weak Model Assumptions: An Application to the Works of Two Baroque Masters. In B. E. Rogowitz and T. N. Pappas (eds.), Electronic Imaging: Human Vision and Electronic Imaging XIV, vol. 7240, pp. 72401M112. SPIE/IS\&T, Bellingham, 2009. Online.
B. Sankaran, M. Ghazvininejad, X. He, D. Kale, and L. Cohen. Learning and Optimization with Submodular Functions. Report. arXiv.
D. Kale. Unsupervised Pattern Discovery in Sparsely Sampled Clinical Time Series. At From Data to Knowledge Workshop, University of California Berkeley, 2012. Slides. Video.
D. Kale, B. Marlin, R. Khemani, and R. Wetzel. A Novel Application of Unsupervised Learning to Electronic Health Care Records Data. At 1st Southern California Workshop on Machine Learning (SoCaML 2011), 2011.
D. Kale, B. Marlin, R. Khemani, and R. Wetzel. Using Probabilistic Clustering to Find Patterns in Digital Medical Data. At 1st Meaningful Use of Complex Medical Data Symposium (MUCMD 2011), 2011. Video.
A. Hart, D. Kale, R. Khemani, and H. Kincaid. Distributed, Modular Grid Software for Data Management and Exploration of Patient-Centric Healthcare IT Information. O'Reilly Open Source Convention: Special Session on Healthcare Technology (OSCON 2010), 2010.