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Rohit J. Kate, PhD

Associate Professor, Informatics Graduate Program Director
 Rohit Kate
(414) 229-4216
(414) 229-2619
Northwest Quadrant B 6473

Education

Postdoctoral Fellowship Computer Science University of Texas at Austin Austin, Texas 2010
Ph D Computer Science University of Texas at Austin Austin, Texas 2007
MS Computer Science University of Texas at Austin Austin, Texas 2002
B.Tech Computer Science and Engineering Indian Institute of Technology Delhi, India 2000

Speaker Topics

  • Biomedical Informatics
  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Interests & Expertise

Rohit Kate’s research focus is on applying machine learning techniques to do predictive analytics for medical applications. Most recently he has worked on predicting cancer survivability and predicting acute kidney injury using machine learning models. He has also worked on predicting physical activity and energy from time series data obtained using wearable accelerometers.

His research interest is also in applying natural language processing techniques to automate analysis of biomedical and clinical text. In this area, his recent focus has been on leveraging available biomedical knowledge resources to improve techniques for extracting computer-processable knowledge from clinical text.

Selected Publications

Sagheb Hossein Pour, E., & Kate, R. J. (2017, November). Stage-Specific Survivability Prediction Models across Different Cancer Types. In Proceedings of the American Medical Informatics Association (AMIA) Annual Symposium, Washington, DC, November 2017, 1404-1412.
Kate, R. J., & Nadig, R. (2017). Stage-specific predictive models for breast cancer survivability. International Journal of Medical Informatics, 97, 304-311.
Kate, R. J., Swartz, A. M., Welch, W. A., & Strath, S. J. (2016). Comparative evaluation of features and techniques for identifying activity type and estimating energy cost from accelerometer data. Physiological Measurement, 37(3), 360-379.
Kate, R. J. (2016). Using dynamic time warping distances as features for improved time series classification. Data Mining and Knowledge Discovery, 30(2), 283-312.
Kate, R. J., Perez, R. M., Mazumdar, D., Pasupathy, K. S., & Nilakantan, V. (2016). Prediction and Detection Models for Acute Kidney Injury in Hospitalized Older Adults. BMC Medical Informatics and Decision Making, 16:39.
Kate, R. J. (2016). Normalizing clinical terms using learned edit distance patterns. Journal of the American Medical Informatics Association, 23(2), 380-386.
Strath, S. J., Kate, R. J., Keenan, K., Welch, W. A., & Swartz, A. M. (2015). Ngram Time Series Model to Predict Activity Type and Energy Cost from Wrist, Hip and Ankle Accelerometers: Implications of Age. Physiological Measurement, 36(11), 2335-2351.
Kate, R. J. (2013). Towards converting clinical phrases into SNOMED CT expressions. Biomedical Informatics Insights, 6(Suppl1), 29-37.
Kate, R. J. (2012). Unsupervised grammar induction of clinical report sublanguage. Journal of Biomedical Semantics, 3(Suppl 3), S4.
Kate, R. J., Luo, X., Patwardhan, S., Franz, M., Florian, R., Mooney, R. J., Roukos, S., & Welty, C. (2010). Learning to predict readability using diverse linguistic features. Proceedings of the 23rd International Conference on Computational Linguistics (COLING), 546-554.
Kate, R. J., & Mooney, R. J. (2010). Joint entity and relation extraction using card-pyramid parsing. Proceedings of the Fourteenth Conference on Computational Natural Language Learning (CoNLL), 203-212.
Kate, R. J., & Mooney, R. J. (2009). Probabilistic abduction using markov logic networks. Proceedings of IJCAI 2009 Workshop on Plan, Activity, and Intent Recognition (PAIR).
Kate, R. J. (2008). Transforming meaning representation grammars to improve semantic parsing. Proceedings of the Twelfth Conference on Computational Natural Language Learning (CoNLL), 33-40.
Kate, R. J. (2008). A dependency-based word subsequence kernel. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 400-409.
Kate, R. J., & Mooney, R. J. (2007). Learning language semantics from ambiguous supervision. Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI), 895-900.

Courses Taught

  • Computational Intelligence in Health Informatics
  • Essential Programming for Health Informatics
  • Language Technologies in Biomedicine
  • Introduction to Healthcare Informatics
  • Artificial Intelligence in Medicine
  • Natural Language Processing