International System for Total Early Disease Detection (InSTEDD) Platform Taha A. Kass-Hout, M.D., M.S., Nicolas di Tada InSTEDD, Palo Alto, California

1. International System for Total Early Disease Detection (InSTEDD) Platform
Taha A. Kass-Hout, M.D., M.S., Nicolas di Tada
InSTEDD, Palo Alto, California
OBJECTIVE tected in location X, and with a certain spatio-
This paper describes a hybrid (event-based and indi- temporal pattern”). The human input and review
cator-based) surveillance platform designed to module is exposed as a set of functionalities that al-
streamline the collaboration between domain experts lows users to comment, tag, and rank the elements
and machine learning algorithms for detection, pre- (positive, neutral, or negative). Additionally, users
diction and response to health-related events (such as can generate and test multiple hypotheses in parallel,
disease outbreaks). further collect and rank sets of related items (evi-
dence), and model against baseline information (for
BACKGROUND
cyclical or known events). The platform maintains a
Over the last decade, the majority of the designs, list of ongoing possible threats allowing domain ex-
analyses and evaluations of early detection [1] (or perts to focus their field information and either con-
biosurveillance) systems have been geared towards firm or reject the hypotheses created. That feedback
specific data sources and detection algorithms. Much is then fed into the system to update (increase or de-
less effort has been focused on how these systems crease) the reliability of the sources and credibility of
will "interact" with humans [2]. For example, con- the users in light of their inferences or decisions.
sider multiple domain experts working at different
RESULTS
levels across different organizations in an environ-
ment where numerous biosurveillance algorithms The platform synthesizes health-related event indica-
may provide contradictory interpretations of ongoing tors from a wide variety of information sources
events. This paper discusses the anticipated contribu- (structured and unstructured) into a consolidated pic-
tion of social networking, machine learning and col- ture for analysis, maintenance of “community-wide
laboration techniques to address these emerging is- coherence” [3], and collaboration processes. This
sues by drawing upon methods, models and tech- helps detect anomalies, visualize clusters of potential
nologies that have been proven to work in other chal- events, predict the rate and spread of a disease out-
lenging domains. break and provide decision makers with tools, meth-
odologies and processes to investigate the event.
METHODS
Presently, the platform and associated modules are
The platform consists of several high-level modules, being piloted for the Mekong Basin region in SE
including: 1) Data gathering, 2) Automatic feature Asia.
extraction, data classification and tagging, 3) Human
CONCLUSIONS
input, hypotheses generation and review, 4) Predic-
tions and alerts output, and 5) Field confirmation and In this paper we describe a platform that enables de-
feedback. The data gathering module allows users tection, prediction and response to health-related
to collect information from several sources (SMS events through a collaborative approach that com-
messages, RSS feeds, email list (e.g., ProMed), bines data exploration, integration, search and infer-
documents, web pages, electronic medical records, encing – providing more complex analysis and
animal disease data, environmental feed, remote deeper insight. We believe that such a platform repre-
sensing, etc.). The automatic feature extraction, sents the next generation of early detection and re-
data classification and tagging module is an exten- sponse systems.
sible architecture that allows the introduction of ma- REFERENCES
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with human input, these components can suggest
possible events or event types (e.g., at the earliest Further Information:
stages of a disease outbreak: “there is an unknown Taha Kass-Hout, kasshout@instedd.org
respiratory event, transmitted person-to-person, de- http://www.instedd.org
Advances in Disease Surveillance 2008;5:108