New Directions in Artificial Intelligence for Public Health Surveillance

被引：17

作者：

Neill, Daniel B. ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Event & Pattern Detect Lab, HJ Heinz III Coll, Pittsburgh, PA 15213 USA

来源：

IEEE INTELLIGENT SYSTEMS | 2012年 / 27卷 / 01期

基金：

美国国家科学基金会;

关键词：

Surveillance; Diseases; Artificial intelligence; Pattern recognition; Complexity theory; Identity management systems; semantic scan statistic; event detection; disease surveillance; public health surveillance; spatial and subset scanning; DISEASE SURVEILLANCE;

D O I：

10.1109/MIS.2012.18

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The next decade of disease surveillance research will require novel methods to effectively use massive quantities of complex, high-dimensional data. We summarize two recent approaches which deal with the increasing complexity and scale of health data, including the use of rich text data to detect emerging outbreaks with novel symptom patterns, and fast subset scan methods to efficiently identify the most relevant patterns in massive datasets. © 2012 IEEE.

引用

页码：56 / 59

页数：4

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