Summary of Biosurveillance-relevant statistical and data mining technologies

This is directly applicable to a scalar signal (such as “number of respiratory cases today”. This method, more commonly used in computational finance, simply compares the count during the current time period with the weighted average of the counts of recent days. Exponential weighting is typically used, where the half-life is known as the “time window” parameter. This time-window parameter is typically chosen by hand. Hanning window, a low-pass filter, is another commonly used method for computing the trend of a time series. We prefer the Serfling and Univariate HMM methods described below.

[1]  Andrew W. Moore,et al.  Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets , 1998, J. Artif. Intell. Res..

[2]  Gregory F. Cooper,et al.  An overview of the representation and discovery of causal relationships using Bayesian networks , 1999 .

[3]  Andrew W. Moore,et al.  Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees , 1998, NIPS.

[4]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[5]  Christopher J. Miller,et al.  Controlling the False-Discovery Rate in Astrophysical Data Analysis , 2001, astro-ph/0107034.

[6]  R. Serfling Methods for current statistical analysis of excess pneumonia-influenza deaths. , 1963, Public health reports.

[7]  P. Good,et al.  Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses , 1995 .

[8]  Andrew W. Moore,et al.  Rule-based anomaly pattern detection for detecting disease outbreaks , 2002, AAAI/IAAI.

[9]  Gregory F. Cooper,et al.  Causal Discovery from a Mixture of Experimental and Observational Data , 1999, UAI.

[10]  Michael M. Wagner,et al.  Value of ICD-9-Coded Chief Complaints for Detection of Epidemics , 2002, J. Am. Medical Informatics Assoc..

[11]  R. Tibshirani,et al.  An introduction to the bootstrap , 1993 .

[12]  D. Eskin Anomaly Dete tion over Noisy Datausing Learned Probability , 2000 .

[13]  Gregory F. Cooper,et al.  A Bayesian Method for Causal Modeling and Discovery Under Selection , 2000, UAI.

[14]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[15]  Mtw,et al.  Computation, causation, and discovery , 2000 .

[16]  Andrew W. Moore,et al.  Multiresolution Instance-Based Learning , 1995, IJCAI.

[17]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[18]  Lawrence R. Rabiner,et al.  A tutorial on Hidden Markov Models , 1986 .

[19]  M. Kulldorff A spatial scan statistic , 1997 .

[20]  Jon Louis Bentley,et al.  Multidimensional divide-and-conquer , 1980, CACM.

[21]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[22]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[23]  Andrew W. Moore,et al.  A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets , 2000, ICML.

[24]  Stephen M. Omohundro,et al.  Efficient Algorithms with Neural Network Behavior , 1987, Complex Syst..

[25]  Andrew W. Moore,et al.  Efficient Locally Weighted Polynomial Regression Predictions , 1997, ICML.

[26]  Anne Lohrli Chapman and Hall , 1985 .

[27]  Andrew W. Moore,et al.  ADtrees for Fast Counting and for Fast Learning of Association Rules , 1998, KDD.

[28]  James D. Hamilton Time Series Analysis , 1994 .

[29]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1976, TOMS.

[30]  E. Carlstein Nonparametric Change-Point Estimation , 1988 .

[31]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[32]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.