Publications
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(During the COVID-19 pandemic I have not been able to actively update this, but more recent updates are on my Google Scholar profile. I am happy to email PDFs of my papers. Just contact me. )

  1. J. Noh and R. Kavuluru, Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization, Findings of the ACL: EMNLP 2020, pp. 3389-3399 (pdf)

  2. T. Tran and R. Kavuluru, Social Media Surveillance for Perceived Therapeutic Effects of Cannabidiol (CBD) Products, International Journal of Drug Policy 77: 1-8 (2020) (publisher)

  3. CG. Walsh, B. Chaudhry, P. Dua, KW. Goodman, B. Kaplan, R. Kavuluru, A. Solomonides, and V. Subbian, Stigma, Biomarkers, and Algorithmic Bias: Recommendations for Precision Behavioral Health with Artificial Intelligence, JAMIA Open 3 (1): 9-15 (2020) (open access)

  4. PJ. Ward, PJ. Rock, S. Slavova, AM. Young, TL. Bunn, and R. Kavuluru, Enhancing Timeliness of Drug Overdose Mortality Surveillance: A Machine Learning Approach, PLoS ONE 14 (10): 1-12 (2019) (pdf)

  5. A. Rios, EB. Durbin, I. Hands, SM. Arnold, D. Shah, SM. Schwartz, BHL. Goulart, and R. Kavuluru, Cross-Registry Neural Domain Adaptation to Extract Mutational Test Results from Pathology Reports, Journal of Biomedical Informatics 97: 1-8 (2019) (pdf)

  6. T. Tran and R. Kavuluru, Distant Supervision for Treatment Relation Extraction by Leveraging MeSH Subheadings, Artificial Intelligence in Medicine 98: 18-26 (2019) (pdf)

  7. BHL. Goulart, ET. Silgard, CS. Baik, A. Bansal, Q. Sun, EB. Durbin, I. Hands, D. Shah, SM. Arnold, SD. Ramsey, R. Kavuluru, and SM. Schwartz, Validity of Natural Language Processing for Ascertainment of EGFR and ALK Test Results in SEER Cases of Stage IV Non-Small-Cell Lung Cancer, JCO Clinical Cancer Informatics (2019), pp. 1-15 (publisher)

  8. A. Rios and R. Kavuluru, Neural Transfer Learning for Assigning Diagnosis Codes to EMRs, Artificial Intelligence in Medicine 96: 116-122 (2019) (pdf)

  9. M. Gaur, A. Alambo, JP. Sain, U. Kursuncu, K. Thirunarayan, R. Kavuluru, AP. Sheth, RS. Welton, and J. Pathak, Knowledge-Aware Assessment of Severity of Suicide Risk for Early Intervention, Proceedings of The Web Conference (WWW 2019), pp. 514-525 (pdf)

  10. M. Ickes, JW. Hester, AT. Wiggins, MK. Rayens, EJ. Hahn, R. Kavuluru, Prevalence and Reasons for Juul Use among College Students, Journal of American College Health (2019), pp. 1-5 (publisher)

  11. R. Islamaj-Dogan et al. (including T. Tran and R. Kavuluru), Overview of the BioCreative VI Precision Medicine Track: Mining Protein Interactions and Mutations for Precision Medicine, OUP Database (2019), pp. 1-17 (open access)

  12. R. Kavuluru, S. Han, and EJ. Hahn, On the Popularity of the USB Flash-Drive Shaped Electronic Cigarette Juul, Tobacco Control 28 (1): 110-112 (2019) (pdf) [FDA's letter to JUUL Labs citing our work]
    Press coverage: Reuters, FairWarning, Economist
  13. J. Noh and R. Kavuluru, Document Retrieval for Biomedical Question Answering with Neural Sentence Matching, Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA 2018), pp. 194-201 (pdf).

  14. T. Tran and R. Kavuluru, An End-to-End Deep Learning Architecture for Extracting Protein-Protein Interactions Affected by Genetic Mutations, OUP Database (2018), pp. 1-13 (open access).

  15. A. Rios and R. Kavuluru, Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces, Proceedings of the 18th annual conference on Empirical Methods in Natural Language Processing (EMNLP 2018), pp. 3132-3142 (pdf).

  16. A. Sarker et al. (including UKNLP team members S. Han, T. Tran, A. Rios, and R. Kavuluru), Data and Systems for Medication-Related Text Classification and Concept Normalization from Twitter: Insights from the Social Media Mining for Health (SMM4H) 2017 Shared Task, Journal of American Medical Informatics Association 25 (10), 1274-1283 (2018) (open access).

  17. Y. Peng, A. Rios, R. Kavuluru, and Z. Lu, Extracting Chemical–Protein Relations with Ensembles of SVM and Deep Learning Models, OUP Database (2018), pp. 1-9 (open access)

  18. G. Bakal, P. Talari, EV. Kakani, and R. Kavuluru, Exploiting Semantic Patterns over Biomedical Knowledge Graphs for Predicting Treatment and Causative Relations, Journal of Biomedical Informatics 82: 189-199 (2018) (pdf)

  19. A. Rios and R. Kavuluru, EMR Coding with Semi-Parametric Multi-Head Matching Networks, Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2018), pp. 2081-2091 (pdf).

  20. A. Rios, T. Tran, and R. Kavuluru, Predicting Psychological Health from Childhood Essays with Convolutional Neural Networks for the CLPsych 2018 Shared Task (Team UKNLP), Proceedings of the Fifth Workshop on Computational Linguistics & Clinical Psychology: From Keyboard to Clinic (CLPsych 2018), pp. 107-112 (pdf)

  21. A. Rios, R. Kavuluru, and Z. Lu, Generalizing Biomedical Relation Classification with Neural Adversarial Domain Adaptation, Bioinformatics 34 (17), 2973-2981 (2018) (pdf)

  22. S. Han, T. Tran, A. Rios, and R. Kavuluru, Team UKNLP: Detecting ADRs, Classifying Medication In-take Messages, and Normalizing ADR Mentions on Twitter, Proceedings of the 2nd Social Media Mining for Health Applications workshop at AMIA 2017. (report: pdf) [Among 11 teams, our system ranked second in classifying medication intake messages and fourth in detecting adverse drug reaction messages on Twitter]

  23. T. Tran and R. Kavuluru, Exploring a Deep Learning Pipeline for the BioCreative VI Precision Medicine Task, Proceedings of the BioCreative VI workshop, Task 4 on mining protein interactions and mutations for precision medicine (2017), pp. 107-110. (report: pdf) [Among six teams, our system placed second with both exact and homolog level gene matching in extracting PPIs changed by genetic mutations]

  24. Y. Peng, A. Rios, R. Kavuluru, and Z. Lu, Chemical-Protein Relation Extraction with Ensembles of SVM, CNN, and RNN Models, Proceedings of BioCreative VI workshop, Task 5 on text mining chemical-protein interactions (2017), pp 147-15o. (report: pdf) [Out of 13 teams, our system ranked first in extracting chemical-protein interactions from text]

  25. J. Noh and R. Kavuluru, Team UKNLP at TREC 2017 precision medicine track: A knowledge-based IR system with tuned query-time boosting, Proceedings of the Text REtrieval Conference (TREC 2017). (pdf) [Out of runs from 29 teams, our system ranked 6th in the scientfic abstract retrieval task]

  26. T. Tran and R. Kavuluru, Supervised Approaches to Assign Cooperative Patent Classification (CPC) Codes to Patents, Proceedings of the 5th International Conference on Mining Intelligence and Knowledge Exploration (MIKE 2017), pp. 22-34 (pdf).

  27. J. Bopaiah and R. Kavuluru,  Precision/Recall Trade-Off Analysis in Abnormal/Normal Heart Sound Classification, Proceedings of the 5th Intl. Conference on Big Data Analytics (BDA 2017), pp. 179-194. (pdf)

  28. AKM. Sabbir, A. Jimeno-Yepes, and R. Kavuluru, Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept Embeddings, Proceedings of the 17th IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE 2017), pp. 163-170 (pdf)

  29. R. Kavuluru, A. Rios, and T. Tran, Extracting Drug-Drug Interactions with Word and Character-Level Recurrent Neural Networks, Proceedings of the 5th IEEE International Conf. on Healthcare Informatics, Workshop on Healthcare Knowledge Discovery and Management (IEEE ICHI 2017), pp. 5-12 (pdf)

  30. T. Tran and R. Kavuluru, Predicting Mental Conditions Based on "History of Present Illness" in Psychiatric Notes with Deep Neural Networks, Journal of Biomedical Informatics 75: S138-S148 (2017) (preprint) (publisher)

  31. A. Rios and R. Kavuluru, Ordinal Convolutional Neural Networks for Predicting RDoC Positive Valence Psychiatric Symptom Severity Scores, Journal of Biomedical Informatics 75: S85-S93 (2017) (preprint) (publisher) [An earlier version of the predictive model presented in this paper placed 3rd (among 24 teams) in the 2016 CEGS N-GRID NLP Shared Task. The improved version that is described in the paper is the 2nd best performer (as of 5/10/2017) and scores within 1% of the top performance on the shared task.]

  32. D. Harris, R. Kavuluru, J. Jaromczyk, and T. R. Johnson, Rapid and Reusable Text Visualization and Exploration Development with DELVE, Proceedings of the American Medical Informatics Association's Joint Summits (AMIA CRI 2017), pp. 139-148 (pdf)

  33. G. Bakal and R. Kavuluru, On Quantifying Diffusion of Health Information on Twitter, Proceedings of 4th IEEE International Conference on Biomedical & Health Informatics (IEEE BHI 2017), pp. 485-488 (pdf)

  34. S. Han and R. Kavuluru, Exploratory Analysis of Marketing and Non-Marketing E-Cigarette Themes on Twitter, Proceedings of the 8th International Conference on Social Informatics (SocInfo 2016), LNCS 10047, pp. 307-322 (pdf)

  35. R. Kavuluru, M. Ramos-Morales, T. Holaday, AG. Williams, L. Haye, and J. Cerel, Classification of Helpful Comments on Online Suicide Watch Forums, Proceedings of 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB 2016), pp. 32-40 (pdf)

  36. O. Abar, RJ. Charnigo, A. Rayapati, and R. Kavuluru, On Interestingness Measures for Mining Statistically Significant and Novel Clinical Associations from EMRs, Proceedings of the 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Workshop on Methods and Applications in Healthcare Analytics (ACM BCB 2016), pp. 587-594 (pdf)

  37. R. Kavuluru and A.K.M. Sabbir, Toward Automated E-cigarette Surveillance: Spotting E-cigarette Proponents on Twitter, Journal of Biomedical Informatics 61: 19-26 (2016) (preprint) (publisher)
    Press coverage: Daily Caller, San Diego Union Tribune, Altmetric

  38. G. Bakal and R. Kavuluru, Predicting Treatment Relations with Semantic Patterns over Biomedical Knowledge Graphs, 3rd International Conference on Mining Intelligence and Knowledge Exploration (MIKE 2015), LNCS 9468, pp. 586-596 (pdf)

  39. A. Rios and R. Kavuluru, Analyzing the Moving Parts of a Large-Scale Multi-Label Text Classification Pipeline: Experiences in Indexing Biomedical Articles, Proceedings of 3rd IEEE International Conference on Healthcare Informatics (IEEE ICHI 2015), pp. 1-7 (pdf) [Best paper finalist, the system described in the paper also placed 2nd in the 2nd batch of BioASQ 2015]

  40. A. Rios and R. Kavuluru, Convolutional Neural Networks for Biomedical Text Classification: Application in Indexing Biomedical Articles, Proceedings of 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB 2015), pp. 258-267 (pdf)

  41. R. Kavuluru and A. Rios, Automatic Assignment of Non-Leaf MeSH Terms to Biomedical Articles, Proceedings of the AMIA annual symposium (AMIA 2015), pp. 697-706 (pdf)

  42. R. Kavuluru, A. Rios, and Y. Lu, An Empirical Evaluation of Supervised Learning Approaches in Assigning Diagnosis Codes to Electronic Medical Records, Artificial Intelligence in Medicine 65(2): 155-166 (2015) (preprint) (publisher)

  43. S. Han and R. Kavuluru, On Assessing the Sentiment of General Tweets, Proceedings of the 28th Canadian Conference on Artificial Intelligence (Canadian AI 2015), LNCS 9091, pp. 181-195 (pdf)

  44. D. Cameron, R. Kavuluru, T. C. Rindflesch, A. P. Sheth, K. Thirunarayan, and O. Bodenreider, Context-Driven Automatic Subgraph Creation for Literature-Based Discovery, Journal of Biomedical Informatics 54: 141-157 (2015) (preprint) (publisher)

  45. R. Kavuluru and Y. Lu, Leveraging Output Term Co-occurrence Frequencies and Latent Associations in Predicting Medical Subject Headings, Data and Knowledge Engineering 94 (B): 189-201 (2014) (preprint) (publisher)

  46. D. Harris, D. Henderson, R. Kavuluru, A. Stromberg, and T. Johnson, Using Common Table Expressions to Build a Scalable Boolean Query Generator for Clinical Data Warehouses, IEEE Journal of Biomedical and Health Informatics 18 (5): 1607-1613, (2014) (pdf)

  47. Z. Yu, T. Johnson, and R. Kavuluru, Phrase Based Topic Modeling for Semantic Information Processing in Biomedicine, Proceedings of the 12th IEEE International Conference on Machine Learning and Applications, ICMLA 2013, pp. 440-445 (pdf)

  48. A. Rios and R. Kavuluru, Supervised Extraction of Diagnosis Codes from EMRs: Role of Feature Selection, Data Selection, and Probabilistic Thresholding, Proceedings of the IEEE International Conference on Healthcare Informatics (IEEE ICHI 2013), pp. 66-73. (pdf)

  49. R. Kavuluru and Z. He, Unsupervised Medical Subject Heading Assignment Using Output Label Co-Occurrence Statistics and Semantic Predications, Proceedings of the 18th International Conference on Applications of Natural Language to Information Systems (NLDB 2013), LNCS 7934, pp. 176-188. (pdf)

  50. R. Kavuluru, S. Han, and D. Harris, Unsupervised Extraction of Diagnosis Codes from EMRs Using Knowledge-Based and Extractive Text Summarization Techniques, Proceedings of the 26th Canadian Conference on Artificial Intelligence (Canadian AI 2013) LNCS 7884, pp. 77-88. (pdf)

  51. A. Rios, R. Vanderpool, P. Shaw, and R. Kavuluru, A Multi-Label Classification Approach to Coding Cancer Information Service Chat Transcripts, Proceedings of 26th International Florida AI Research Society conference (FLAIRS-2013), pp. 338-343. (pdf)

  52. R. Kavuluru, I. Hands, E. Durbin, and L. Witt, Automatic Extraction of ICD-O-3 Primary Sites from Cancer Pathology Reports, Proceedings of the American Medical Informatics Association's Joint Summits (AMIA CRI 2013), pp. 112-116. (pdf)

  53. R. Kavuluru and D. Harris, A Knowledge-Based Approach to Syntactic Disambiguation of Biomedical Noun Compounds, proceedings of the 24th international conference on computational linguistics (COLING 2012), pp. 559-567. (pdf)

  54. R. Kavuluru, C. Thomas, A. Sheth, V. Chan, W. Wang, A. Sato, and A. Walters, An Up-to-Date Knowledge-Based Literature Search and Exploration Framework for Focused Bioscience Domains, proceedings of IHI 2012, 2nd ACM Intl. Health Informatics Symp., pp. 275-284. (pdf)

  55. D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, and K. Thirunarayan, Semantic Predications for Complex Information Needs in Biomedical Literature, proceedings of BIBM 2011, the 5th IEEE conference on Bioinformatics and Biomedicine, pp. 512-519. (pdf)

  56. K. Chen, R. Kavuluru, S. Guo, RASP: Efficient Multidimensional Range Query on Attack-Resilient Encrypted Databases, Proceedings of the 1st ACM Conference on Data and Application Security and Privacy (CODASPY 2011), pp. 249-260. (pdf)

  57. R. Kavuluru, Characterization of 2n-Periodic Binary Sequences with Fixed 2-error or 3-error Linear Complexity, Designs, Codes, and Cryptography 53(2) (2009) 75-97 (pdf)
    Note: Counting functions in Theorems 4.2 and 5.2 in this paper are not correct in that they do not hold for all fixed 3-error linear complexity values. Many thanks to Zhou and Liu for addressing this.

  58. R. Kavuluru and A. Klapper, Lower Bounds on Error Complexity Measures for Periodic LFSR and FCSR Sequences, Cryptography and Communications 1 (2009) 95-116. (pdf)

  59. R. Kavuluru and A. Klapper, Counting Functions for the k-error Linear Complexity of 2n-Periodic Binary Sequences, Proceedings of the 15th Annual Workshop on Selected Areas in Cryptography SAC 2008, LNCS 5389 (2009) 151-164 (pdf)

  60. R. Kavuluru, 2n-Periodic Binary Sequences with Fixed k-error Linear Complexity for k=2 or 3, Proceedigns of the 5th Intl Conference on Sequences and Their Applications (SETA 2008), LNCS 5203, pp. 252-265.

  61. R. Kavuluru and A. Klapper, On the k-operation Linear Complexity of Periodic Sequences, Proceedings of the 8th Intl Conference on Progress in Cryptology (INDOCRYPT 2007), LNCS 4859, pp. 322-330.

  62. Y. Diao, C. Ernst, R. Kavuluru, and U. Ziegler, Numerical Upper Bounds on Rope Lengths of Large Physical Knots, Journal of Physics. A: Math. Gen 39 (2006) 4829-4843. (pdf)