Developing a disease outbreak event corpus.

Background: In recent years, there has been a growth in work on the use of information extraction technologies for tracking disease outbreaks from online news texts, yet publicly available evaluation standards (and associated resources) for this new area of research have been noticeably lacking.

Objective: This study seeks to create a "gold standard" data set against which to test how accurately disease outbreak information extraction systems can identify the semantics of disease outbreak events. Additionally, we hope that the provision of an annotation scheme (and associated corpus) to the community will encourage open evaluation in this new and growing application area.

A framework for enhancing spatial and temporal granularity in report-based health surveillance systems.

Background: Current public concern over the spread of infectious diseases has underscored the importance of health surveillance systems for the speedy detection of disease outbreaks. Several international report-based monitoring systems have been developed, including GPHIN, Argus, HealthMap, and BioCaster. A vital feature of these report-based systems is the geo-temporal encoding of outbreak-related textual data.

Classifying disease outbreak reports using n-grams and semantic features.

Introduction: This paper explores the benefits of using n-grams and semantic features for the classification of disease outbreak reports, in the context of the BioCaster disease outbreak report text mining system. A novel feature of this work is the use of a general purpose semantic tagger - the USAS tagger - to generate features.

Background: We outline the application context for this work (the BioCaster epidemiological text mining system), before going on to describe the experimental data used in our classification experiments (the 1000 document BioCaster corpus).

Towards role-based filtering of disease outbreak reports.

This paper explores the role of named entities (NEs) in the classification of disease outbreak report. In the annotation schema of BioCaster, a text mining system for public health protection, important concepts that reflect information about infectious diseases were conceptually analyzed with a formal ontological methodology and classified into types and roles. Types are specified as NE classes and roles are integrated into NEs as attributes such as a chemical and whether it is being used as a therapy for some infectious disease.

BioCaster: detecting public health rumors with a Web-based text mining system.

Summary: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Layman's terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes.

Structuring an event ontology for disease outbreak detection.

Background: This paper describes the design of an event ontology being developed for application in the machine understanding of infectious disease-related events reported in natural language text. This event ontology is designed to support timely detection of disease outbreaks and rapid judgment of their alerting status by 1) bridging a gap between layman's language used in disease outbreak reports and public health experts' deep knowledge, and 2) making multi-lingual information available.

Construction And Content: This event ontology integrates a model of experts' knowledge for disease surveillance, and at the same time sets of linguistic expressions which denote disease-related events, and formal definitions of events.

A multilingual ontology for infectious disease surveillance: rationale, design and challenges.

A lack of surveillance system infrastructure in the Asia-Pacific region is seen as hindering the global control of rapidly spreading infectious diseases such as the recent avian H5N1 epidemic. As part of improving surveillance in the region, the BioCaster project aims to develop a system based on text mining for automatically monitoring Internet news and other online sources in several regional languages. At the heart of the system is an application ontology which serves the dual purpose of enabling advanced searches on the mined facts and of allowing the system to make intelligent inferences for assessing the priority of events.