Publications

[Cami 2008a] Cami A, Wallstrom GL, Hogan WR. Effect of commuting on the detection and characterization performance of the Bayesian aerosol release detector. In: Proceedings of the Bioinformatics and Biomedicine Workshops, IEEE International Conference (2008) 91 -98. [pdf]

[Cami 2008b] Cami A, Wallstrom GL, Hogan WR. Integrating a commuting model with the Bayesian aerosol release detector. In: Proceeding of BioSecure (2008) 85-96. [pdf]

[Chen 2008] Chen L, Dubrawski A, Ray S, Baysek M, Kelley L, Dunham A, Huckabee M, Fedorka-Cray PJ, Jackson C, McGlinchey B. Detecting linkages between human illness and Salmonella isolates in food using a new tool for spatio-temporal analysis of multistream data. In: Proceedings of the Symposium of the American Medical Informatics Association (2008) 900. [pdf]

[Cooper 2004] Cooper GF, Dash DH, Levander JD, Wong WK, Hogan WR, and Wagner MM. Bayesian biosurveillance of disease outbreaks. Proceedings of the Conference on Uncertainty in Artificial Intelligence (2004) 94-103. [pdf]

[Cooper 2006] Cooper GF, Dowling JN, Levander JD, Sutovsky P. A Bayesian algorithm for detecting CDC Category A outbreak diseases from emergency dept chief complaints. In:Advances in Disease Surveillance 2 (2006) 45. [pdf]

[Das 2007] Das K, Schneider J. Detecting anomalous records in categorical datasets. In: Proceeding of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining  (2007) 220-229. [pdf]

[Das 2008a] Das K, Schneider J, Neill D. Anomaly pattern detection in categorical datasets. In: Proceeding of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2008) 169-176. [pdf]

[Das 2008b] Das K, Schneider J, Neill DB. Anomaly pattern detection for biosurveillance. Advances in Disease Surveillance 5 (2008) 19. [pdf]

[Das 2009] Das K. Anomalous Pattern Detection. Doctoral Dissertation, Machine Learning Department, Carnegie Mellon University (2009). [pdf]

[Dubrawski  2007a] Dubrawski A, Baysek M, Mikus S, McDaniel C, Mowry B, Moyer L, Ostlund J, Sondheimer N, Stewart T. Applying outbreak detection algorithms to prognostics. In: Proceedings of the AAAI Fall Symposium on Artificial Intelligence in Prognostics (2007). [pdf]

[Dubrawski  2007b] Dubrawski A, Ostlund J, Chen L, Moore AW. Computationally efficient scoring of activity in large social networks using connectivity patterns and demographics of entities. In: Proceedings of the Workshop on Artificial Intelligence and Data Mining (2007). [pdf]

[Dubrawski 2007c] Dubrawski A, Sabhnani M, Ray S, Roure J, XBaysek M, T-Cube as an enabling technology in surveillance applications, Advances in Disease Surveillance 4 (2007) 6. [pdf

[Dubrawski 2008a] Dubrawski A, Sabhnani M, Ray S, Baysek M, Chen L, Ostlund J, Knight M. Interactive manipulation, visualization and analysis of large sets of multidimensional time series in health informatics. In: Proceedings of the INFORMS Workshop on Data Mining and Health Informatics (2008). [pdf]

[Dubrawski 2008b] Dubrawski A, Chen L, Ostlund J. Using the AFDL algorithm to estimate the risk of positive outcomes of microbial tests at food establishments. Advances in Disease Surveillance 5 (2008) 102. [pdf]

[Dubrawski 2009a] Dubrawski A. Detection of events in multiple streams of surveillance data. In: Castillo-Chavez C, Chen H, Lober W, Thurmond M, and Zeng D (eds.) Infectious Disease Informatics: Public Health and Biodefense (Springer-Verlag, 2009 in press). [pdf]

[Dubrawski 2009b] Dubrawski A, Sabhnani M, Knight M, Baysek M, Neill D, Ray S, Michalska A, Waidyanatha N. T-Cube web interface in support of a real-time bio-surveillance program, (extended demo abstract). In: Proceedings of the IEEE/ACM International Conference on Information and Communication Technologies and Development (2009). [pdf]

[Dubrawski 2009c] Dubrawski A, Sarkar P, Chen L. Trade-offs between agility and reliability of predictions in dynamic social networks used to model risk of microbial contamination of food. In: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, Athens, Greece (2009). [pdf]

[Espino 2007] Espino J, Dowling J, Levander J, Sutovsky P, Wagner MW, Cooper GF, SyCo: A Probabilistic Machine Learning Method for Classifying Chief Complaints into Symptom and Syndrome Categories,  Advances in Disease Surveillance 2(2007) 5. [pdf]

[Hogan 2006] Hogan WR. Chapter 19: Atmospheric dispersion modeling in biosurveillance. In: Wagner MM, Moore AW, Aryel RM (eds.) Handbook of Biosurveillance (Academic Press, 2006). [pdf]

[Hogan 2007] Hogan WR, Cooper GF, Wallstrom GL, Wagner MM, Depinay JM. The Bayesian aerosol release detector: An algorithm for detecting and characterizing outbreaks caused by an atmospheric release of Bacillus Anthracis. Statistics in Medicine 26 (2007) 5225-5252. [pdf]

[Jaing 2006] Jiang X, Wallstrom GL. A Bayesian Network for Outbreak Detection and Prediction. Proceedings of AAAI-06 (2006) 1155-1160. [pdf]

[Jaing 2007a] Jiang X, Cooper GF, Levander J. A Bayesian network model for spatial cluster detection. UAI Workshop on Bayesian Modeling Applications (2007). [pdf]

[Jiang 2007b] Jiang, X, and Cooper, GF. A recursive algorithm for spatial cluster detection. In: Proceedings of the Symposium of the American Medical Informatics Association (AMIA) (2007): 369-373. [pdf]

[Jiang 2008a] Jiang X. A Bayesian Network Model for Spatiotemporal Event Detection. Doctoral dissertation, Department of Biomedical Informatics, University of Pittsburgh (2008).

[Jiang 2008b] Jiang X, Cooper GF. A temporal method for outbreak detection using a Bayesian network. Advances in Disease Surveillance 5 (2008) 105. [pdf]

[Jiang 2008c] Jiang X, Wagner MM, Cooper GF, Modeling the temporal trend of the daily severity of an outbreak using Bayesian networks, Innovations in Bayesian Networks of Studies in Computational Intelligence, Springer-Verlag, (2008). [link]

[Jiang 2009a] Jiang XI, Neill DB, Cooper GF. On the robustness of Bayesian network based spatial event surveillance. International Journal of Approximate Reasoning (to appear).

[Jiang 2009b] Jiang X, Cooper GF. A real-time temporal Bayesian architecture for event surveillance and its application to patient-specific multiple disease outbreak detection. Data Mining and Knowledge Discovery (to appear). [pdf]

[Jiang 2009c] Jiang X, Cooper GF. A Bayesian spatio-temporal method for disease outbreak detection (under review).

[Jiang 2009d] Jiang X, Cooper GF, Neill DB. Generalized AMOC curves for evaluation and improvement of event surveillance. In: Proceedings of the Symposium of the American Medical Informatics Association (2009). [pdf]

[Kong 2008] Kong X, Wallstrom GL, Hogan WR. A temporal extension of the Bayesian aerosol release detector. In: Proceedings of BioSecure (2008) 97–107. [pdf]

[Kulldorf 2007] Kulldorff M, Mostashari F, Luiz FD, Yih K, Kleinman K, Platt R. Multivariate scan statistics for disease surveillance. Statistics in Medicine 26 (2007). [pdf]

[Makatchev 2008a] Makatchev M, Neill D. Learning outbreak regions in Bayesian spatial scan statistics. In: Proceedings of the ICML/UAI/COLT Workshop on Machine Learning for Health Care Applications (2008). [pdf]

[Makatchev 2008b] Makatchev M, Neill DB. Learning outbreak regions for Bayesian spatial biosurveillance. Advances in Disease Surveillance 5 (2008) 45. [pdf]

[Neill 2004a] Neill DB, Moore AW. A fast multi-resolution method for detection of significant spatial disease clusters. Advances in Neural Information Processing Systems 16 (2004) 651-658. [pdf]

[Neill 2004b] Neill DB, Moore AW. Rapid detection of significant spatial clusters. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2004) 256-265. [pdf]

[Neill 2005a] Neill DB, Moore AW. Efficient scan statistic computations. In A. Lawson and K. Kleinman (eds.) Spatial and Syndromic Surveillance for Public Health (2005) 189-202.

[Neill 2005b] Neill DB, Moore AW, Pereira F, Mitchell T. Detecting significant multidimensional spatial clusters. Advances in Neural Information Processing Systems 17 (2005) 969-976. [pdf]

[Neill 2005c] Neill DB, Moore AW, Sabhnani MR, Daniel K. Detection of emerging space-time clusters. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining(2005) 218-227. [pdf]

[Neill 2006a] Neill DB. Detection of Spatial and Spatio-Temporal Clusters. Doctoral Disseration, School of Computer Science, Carnegie Mellon University (2006). [pdf]

[Neill 2006b] Neill DB, Moore AW, Cooper GF. A Bayesian spatial scan statistic. Neural Information Processing Systems18 (2006) 1003-1010. [pdf]

[Neill 2006c] Neill DB, Moore AW, and Cooper GF. A Bayesian scan statistic for spatial cluster detection. Advances in Disease Surveillance 1 (2006) 55. [pdf]

[Neill 2007a] Neill DB, Sabhnani MR. A robust expectation-based spatial scan statistic. Advances in Disease Surveillance2 (2007) 61. [pdf]

[Neill 2007b] Neill DB, Lingwall J. A nonparametric scan statistic for multivariate disease surveillance. Advances in Disease Surveillance 4 (2007) 106. [pdf]

[Neill 2007c] Neill DB, Moore AW, Cooper GF. A multivariate Bayesian scan statistic. Advances in Disease Surveillance 2 (2007) 60. [pdf]

[Neill 2007d] Neill DB. Incorporating learning into disease surveillance systems. Advances in Disease Surveillance 4 (2007) 107. [pdf]

[Neill 2008] Neill DB. Fast and flexible outbreak detection by linear-time subset scanning. Advances in Disease Surveillance 5 (2008) 48. [pdf]

[Neill 2009a] Neill DB. Expectation-based scan statistics for monitoring spatial time series data. International Journal of Forecasting 25 (2009) 498-517. [pdf]

[Neill 2009b] Neill DB. An empirical comparison of spatial scan statistics for outbreak detection. International Journal of Health Geographics 8 (2009) 20. [pdf]

[Neill 2009c] Neill DB, Cooper GF. A multivariate Bayesian scan statistic for early event detection and characterization. Machine Learning (in press). [pdf]

[Neill 2009d] Neill DB, Cooper GF, Das K, Jiang X, Schneider J. Bayesian network scan statistics for multivariate pattern detection. In: J. Glaz, V. Pozdnyakov, and S. Wallenstein (eds.) Scan Statistics: Methods and Applications (Birkhäuser, 2009). [pdf]

[Que 2008a] Que J, Tsui FC. A multilevel spatial clustering algorithm for detection of disease outbreaks. In: Proceedings to the Symposium of the American Medical Informatics Association (2008) 611-615. [pdf]

[Que 2008b] Que J, Tsui FC, Espino J. A Z-Score based multi-level spatial clustering algorithm for the detection of disease outbreaks. In: Proceedings of Biosecure (2008) 108-118. [pdf]

[Que 2009] Que J, Tsui FC. Rank-based spatial clustering: A framework for rapid outbreak detection (under review).

[Ray 2008] Ray S, Michalska A, Sabhnani M, Dubrawski A, Baysek M, Chen L, Ostlund J. Tcube web interface: A tool for immediate visualization, interactive manipulation and analysis of large sets of multivariate time series. In: Proceedings of the Symposium of the American Medical Informatics Association (2008) 1106.

[Rolka 2007] Rolka H, Burkom H, Cooper GF, Kulldorff M, Madigan D, Wong WK. Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: Research needs. Statistics In Medicine 26 (2007) 1834-1856. [pdf]

[Roure 2007a ] Roure J, Dubrawski A, Schneider J. A study into detection of bio-events in multiple streams of surveillance data. In D. Zeng et al. (eds.): BioSurveillance 2007, Lecture Notes in Computer Science 4506 (2007) 124–133. [pdf]

[Roure 2007b] Roure J, Dubrawski A, Schneider J. Learning specific detectors of adverse events in multivariate time series. Advances in Disease Surveillance 4 (2007) 111. [pdf]

[Roure 2008] Roure J, Dubrawski A, Schneider J. Learning detectors of events in multivariate time series. In: Proceedings of the Symposium of the American Medical Informatics Association (2008) 171. [pdf]

[Sabhanani 2005a] Sabhnani MR, Neill DB, et al. Detecting anomalous patterns in pharmacy retail data. Proceedings of the KDD Workshop on Data Mining Methods for Anomaly Detection 7 (2005) 132-137. [pdf]

[Sabhanani 2005b] Sabhnani MR, Neill DB, Moore AW, Dubrawski AW, Wong WK. Efficient analytics for effective monitoring of biomedical security. Proceedings of the IEEE International Conference on Information and Automation (2005) 87-92. [pdf]

[Sabhnani 2007a] Sabhnani M, Dubrawski A, Schneider J. Multivariate time series analyses using primitive univariate algorithms. Advances in Disease Surveillance 4 (2007)  112. [pdf]

[Sabhnani 2007b] Sabhnani MR, Moore AW, and Dubrawski A. Rapid processing of ad-hoc queries against large sets of time series. Advances in Disease Surveillance 2 (2007) 66. [pdf]

[Sahin 2009] Sahin I, Tsui FC. Evaluation of the Bayesian aerosol release detector using the second order closure integrated PUFF and the fifth-generation mesoscale model (in preparation).

[Sarkar 2005] Sarkar P, Moore A. Dynamic social network analysis using latent space models. ACM SIGKDD Explorations Newsletter 7 (2005) 31-40. [pdf]

[Sarkar 2008] Sarkar P, Chen L, Dubrawski A. Dynamic network model for predicting occurrences of Salmonella at food facilities. In: Proceedings of BioSecure (2008) 56–63. [pdf]

[Shen 2005] Shen Y, Wong WK, Cooper GF. Estimating the expected warning time of outbreak-detection algorithms. In: Advances in Disease Surveillance 1 (2006) 65. [pdf]

[Shen 2006] Shen Y, Wong WK, and Cooper GF. A generalization of the AMOC curve. Advances in Disease Surveillance 1 (2006) 65 [pdf]

[Shen 2007a] Shen Y, Cooper GF. A Bayesian biosurveillance method that models unknown outbreak diseases. Proceedings of Intelligence and Security Informatics: Biosurveillance (2007) 209-215. [pdf]

[Shen 2007b] Shen, Y, Wong WK, Levander J and Cooper GF. An outbreak detection algorithm that efficiently performs complete Bayesian model averaging over all possible spatial distributions of disease. Advances in Disease Surveillance 4 (2007):113. [pdf]

[Shen 2008] Shen Y, Adamou C, Dowling JN, Cooper GF. Estimating the joint disease outbreak-detection time when an automated biosurveillance system is augmenting traditional clinical case finding. Journal of Biomedical Informatics 41 (2008) 224-231. [pdf

[Shen 2009a] Shen Y, Cooper GF. A new prior for Bayesian anomaly detection – Application to biosurveillance. Methods of Information in Medicine (to appear).

[Shen 2009b]Shen Y, Cooper GF. Bayesian modeling of unknown diseases for biosurveillance. In: Proceedings of the Symposium of the American Medical Informatics Association (2009). [pdf]

[Shen 2009c] Shen Y. Bayesian Modeling of Anomalies Due to Known and Unknown Causes. Doctoral Dissertation, Intelligent Systems Program, University of Pittsburgh (2009). [pdf]

[Siddiqui 2007a] Siddiqi S, Boots B, Gordon GJ. A constraint generation approach to learning stable linear dynamical systems. Advances in Neural Information Processing Systems (2007).  [pdf]

[Siddiqui 2007b] Siddiqi S, Boots B, Gordon GJ, Dubrawski AW. Learning stable multivariate baseline models for outbreak detection. Advances in Disease Surveillance 4 (2007) 266. [pdf]     

[Sutovsky 2008] Sutovsky P, Cooper GF. Hierarchical explanation of inference in Bayesian networks that represent a population of independent agents. In: Proceedings of the European Conference on Artificial Intelligence (2008) 214-218. [pdf]

[Tsui 2009] Tsui FC, Dowling J. Evaluation of emergency department reports and chief complaints for influenza detection using Bayesian case detector (in preparation).

[USDA 2008] USDA. Food Safety Inspection Service, U.S. Department of Agriculture: Data analysis for public health risk-based inspection system for processing and slaughter, Appendix E - Data analyses (2008). [pdf]

[Wagner 2006] Wagner MM, Moore AW, Aryel RM (eds.), Handbook of Biosurveillance (Academic Press, 2006).

[Wong 2005a] Wong WK, Cooper GF, Dash DH, Levander JD, Dowling JN, Hogan WR, Wagner MM. Bayesian biosurveillance using multiple data streams. Morbidity and Mortality Weekly Report Supplement 54 (2005) 63-69. [pdf]

[Wong 2005b] Wong WK, Cooper GF, Dash DH, Levander JD, Dowling JN, Hogan WR, and Wagner MM. Population-wide anomaly detection. Data Mining Methods for Anomaly Detection Workshop at the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2005) 79-83. [pdf]

[Wong 2005c] Wong WK, Moore AW, Cooper GF, Wagner MM. What's Strange About Recent Events (WSARE): An algorithm for the early detection of disease outbreaks. Journal of Machine Learning Research 6 (2005) 1961-1998. [pdf]

[Zhang 2008] Zhang Y, Schneider J, Dubrawski A. Learning the semantic correlation: An alternative way to gain from unlabeled text. In: Proceedings of the Conference on Neural Information Processing Systems (NIPS) (2008) 1945-1952. [pdf]


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