Modeling Baseline Shifts in Multivariate Disease Outbreak Detection
نویسندگان
چکیده
منابع مشابه
Modeling Baseline Shifts in Multivariate Disease Outbreak Detection
Methods Existing multivariate algorithms only model disease-relevant data streams (e.g., anti-fever medication sales or patient visits with constitutional syndrome for detection of flu outbreak). On the contrary, we also incorporate a non-disease-relevant data stream as a control factor. We assume that the counts from all data streams follow a Multinomial distribution. Given this distribution, ...
متن کاملLearning Stable Multivariate Baseline Models for Outbreak Detection
OBJECTIVE We propose a novel technique for building generative models of real-valued multivariate time series data streams. Such models are of considerable utility as baseline simulators in anomaly detection systems. The proposed algorithm, based on Linear Dynamical Systems (LDS) [1], learns stable parameters efficiently while yielding more accurate results than previously known methods. The re...
متن کاملTowards Modeling Disease Outbreak Notification Systems
Disease outbreak detection, monitoring and notification systems play an important role in assessing threats to public health since disease outbreaks are becoming increasingly common world-wide. There are several systems in use around the world, with coverage of national, international and global disease outbreaks. These systems use different taxonomies and classifications for the detection and ...
متن کاملInfectious Disease Informatics and Outbreak Detection
Daniel Zeng, Hsinchun Chen, Cecil Lynch, Millicent Eidson, and Ivan Gotham 1 Management Information Systems Department, Eller College of Management, University of Arizona, Tucson, Arizona 85721; 2 Division of Medical Informatics, School of Medicine, University of California, Davis, California 95616; also with California Department of Health Services; 3 New York State Department of Health, Alban...
متن کاملEnsemble Forecasting for Disease Outbreak Detection
We describe a method to improve detection of disease outbreaks in pre-diagnostic time series data. The method uses multiple forecasters and learns the linear combination to minimize the expected squared error of the next day's forecast. This combination adaptively changes over time. This adaptive ensemble combination is used to generate a disease alert score for each day, using a separate multi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Online Journal of Public Health Informatics
سال: 2013
ISSN: 1947-2579
DOI: 10.5210/ojphi.v5i1.4571