Anomaly Detection System For Healthcare Resource Usage In Machine Learning
Data mining approaches have been widely applied in the field of healthcare. Patient Medical Records contains vast clinical informations about patient conditions along with treatment and its procedure .Systematic healthcare utilization analysis use these observational datas to guide resource planning and improve the quality of care delivery while reducing cost. Here present a framework for utilization analysis that can be easily applied to the Diabetes population. The framework includes patients profiling with the disease entries and the patient conditions with treatment procedure, and contextual anomaly detection to provide the better healthcare delivery for the normal and abnormal patients by classifying the different kind of clinical characteristics patients in to clusters and form the patterns of disease evolution. Have to provide the corrective actions for the anomalies.
 Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM Comput.Surv.,
41(3):15:1–15:58, July 2009.
 Clifton Phua, Damminda Alahakoon, and Vincent Lee.Minority report in fraud detection: Classification of
skewed data. SIGKDD Explor. Newsl., 6(1):50–59,June 2004.
 Manish Gupta, Jing Gao, Charu C. Aggarwal, and Jiawei Han. Outlier detection for temporal data: A survey.
 V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: A survey. Technical Report TR 07-017, Dept. Of
Computer Engineering, Univ. Minnesota, 2007.
 Charu C Aggarwal. Outlier analysis. Springer, 2013.
 M. Eisele, H. van den Bussche, D. Koller, B. Wiese, H. Kaduszkiewicz, W. Maier, G. Glaeske, S. Steinmann, K.
Wegscheider, and G. Sch on. Utilization patterns of ambulatory medical care before and after the diagnosis of
dementia in germany–results of a case-control study. Dement. Geriatr. Cogn. Disord., 29(6):475–483, Jul. 2010.
 J. H. Friedman. Multivariate adaptive regression splines. Annals of Statistics, 19(1):1–67, 1991.
 A. Gawande. The hot spotters. New Yorker, January 2011.
 F. Grubbs. Procedures for detecting outlying observations in samples. Technometrics, 11(1):1–21, 1969.
 M. Hauskrecht, M. Valko, I.Batal, G. Clermont, S. Visweswaran, and G. Cooper. Conditional outlier detection
for clinical alerting. In Proceedings of the Annual American Medical Informatics Association (AMIA) Symposium,
pages 286–290, 2010.
 M. Hauskrecht, M. Valko, B. Kveton, S. Visweswaram, and G. Cooper. Evidence-based anomaly detection in
clinical domains. In Proceedings of the Annual American Medical Informatics Association (AMIA) Symposium,
pages 319–323, 2007.
 A. K. Jain and Richard C. Dubes. Algorithms for Clustering Data. Prentice-Hall, Upper Saddle River, NJ,
 N. Lee, A. Laine, J. Hu, F. Wang, J. Sun, and S. Ebadollahi. Mining electronic medical records to explore the
linkage between healthcare resource utilization and disease severity in diabetic patients. In First IEEE International
Conf. on Health Informatics, Imaging and Systems Biology, 2011.
 Jessica Lin, Eamonn Keogh, Ada Fu, and Helga Van Herle. Approximations to magic: Finding unusual medical
time series. In In 18th IEEE Symp. on Computer-Based Medical Systems (CBMS), pages 23–24, 2005.
 J. B. MacQueen. Some methods for classification and analysis of multivariate observations. In Proceedings of
5th Berkeley Symposium on Mathematical Statistics and Probability, pages 281–297. University of California
 M.R. Moskovitch and Y. Shahar. Medical temporal knowledge discovery vis temporal abstraction. In AMIA Annual
Symposium Proceedings, pages 452–456, 2009.
 W. K. Nicholson, S. A. Ellison, H. Grason, and N. R. Powe. Patterns of ambulatory care use for gynecologic
conditions: A national study. Am. J. Obstet. Gynecol., 184(4):523–530, Mar. 2001.
 K. I. Penny and I. T. Jolliffe. A comparison of multivariate outlier detection methods for clinical laboratory
safety data. Journal of the Royal Statistical Society: Series D (The Statistician), 50(3):295–307, sep 2001.
 H. S. Ruchlin, S. Morris, and J. N. Morris. Resident medical care utilization patterns in continuing care retirement
communities. Health Care Financ Rev., 14(4):151–168, Summer 1993.
 Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis
and Machine Intelligence, pages 888–905, 2000.
 Z. Syed, M. Saeed, and I. Rubinfeld. Identifying high-risk patients without labeled training data: Anomaly detection
methodologies to predict adverse outcomes. In Proceedings of the Annual American Medical Informatics
Association (AMIA) Symposium, pages 772–776, 2010.
 J. Tighe and S. Lazebnik. Superparsing: Scalable nonparametric image parsing with superpixels. In Proceedings
of the 2010 European Conf. on Computer Vision (ECCV), pages 319–323, 2007.
 R. Winkelman and S. Mehmod. A comparative analysis of claims-based tools for health risk assessment. Society
of Actuaries Report, 2007.
 W. K. Wong, A. Moor, G. Cooper, and M. Wagner. Bayesian network anomaly pattern detection for disease
outbreaks. In Proceedings of the 20th International Conference on Machine Learning, pages 808–815, 2003.
 Linli Xu, James Neufeld, Bryce Larson, and Dale Schuurmans. Maximum margin clustering. In Advances in
Neural Information Processing Systems 17, 2004.
 Markus M. Breunig, Hans-Peter Kriegel, Raymond T.Ng, and Jorg Sander. Lof: Identifying density-based local
outliers. SIGMOD Rec., 29(2):93–104, May 2000.
 Jianying Hu,Fei Wang, Jimeng Sun,Robert Sorrentino, and Shahram Ebadollahi,.'A Healthcare Utilization
Analysis Framework for Hot Spotting and Contextual Anomaly Detection'.
This work is licensed under a Creative Commons Attribution 4.0 International License.