Ontology-Centered Syndromic Surveillance for Bioterrorism

Crubézy, Monica; O’Connor, Martin J.; Pincus, Zachary; Musen, Mark A.; Buckeridge, David L.

doi:10.1109/mis.2005.91

Cited by 43 publications

(32 citation statements)

References 8 publications

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“…The interval is set to start from zero, where the model behavior is undefined, to where the parameter no longer has an observable impact, i.e., when it is high enough to exhaust the population regardless of all other parameters. Within the [8,20] interval, changing the context contacts parameter had, in this example, a significant effect on the behavior of the model. Repeating the same series of experiments with a smaller step size within the [8,20] interval, a smaller region of interest was obtained within the [14,16] interval.…”

Section: The Micro-meso-macro Linkmentioning

confidence: 85%

A workflow for software development within computational epidemiology

Cakici

Boman

2011

Journal of Computational Science

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A critical investigation into computational models developed for studying the spread of communicable disease is presented. The case in point is a spatially explicit micro-meso-macro model for the entire Swedish population built on registry data, thus far used for smallpox and for influenzalike illnesses. The lessons learned from a software development project of more than 100 person months are collected into a check list. The list is intended for use by computational epidemiologists and policy makers, and the workflow incorporating these two roles is described in detail.

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Section: The Micro-meso-macro Linkmentioning

confidence: 85%

A workflow for software development within computational epidemiology

Cakici

Boman

2011

Journal of Computational Science

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“…The data on the environment of the individual [4] will be partial and noisy, and probably capture less than 1% of daily life, even in the most ideal (from the perspective of health researchers) of circumstances. Syndromic surveillance, the collective term for capturing early or weak signals of macro-level behaviors and encoding them in quantitative terms, such as the number of ambulance dispatches or the volume of headache remedies sold over the counter in a particular period [21], will likewise constitute a form of sampling. It is difficult to create an efficient and cost-effective system for syndromic surveillance [22,23], but it may contribute towards health goals from a policy point of view in many countries, especially those in which digital data flows and reporting systems are already in place [24].…”

Section: Resultsmentioning

confidence: 99%

Supporting global health goals with information and communications technology

Boman

Kruse

2017

Global Health Action

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The objective of this study is to critically assess the possible roles of information and communications technology (ICT) in supporting global health goals. This is done by considering privilege and connectibility. In short, ICT can contribute by providing health information via four different kinds of access, each with its own history and prospective future. All four are analyzed here, in two perspectives: business-as-usual and disruptive. Health data analytics is difficult since the digital representation of past, current, and future health information is lacking. The flow of analytics that may prove beneficial to the individual and not just meet abstract population-level goals or ambitions is analyzed in detail. Sensemaking is also needed, to meet the minimum requirement of making prospective future services understandable to policymakers. Drivers as well as barriers for areas in which policy decisions have the potential to drive positive developments for meeting the Sustainable Development Goals are identified.

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“…We have to devise ways to map the database tables to our data model OWL classes. In a previous project, the BioSTORM disease-surveillance framework 22 employs techniques to map disparate data sources to a data model. These techniques were developed using a frame-based language and we are translating these methodologies to use OWL and SWRL.…”

Section: Discussionmentioning

confidence: 99%

Towards Semantic Interoperability in a Clinical Trials Management System

Shankar

Martins

O’Connor

et al. 2006

Lecture Notes in Computer Science

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Abstract.Clinical trials are studies in human patients to evaluate the safety and effectiveness of new therapies. Managing a clinical trial from its inception to completion typically involves multiple disparate applications facilitating activities such as trial design specification, clinical sites management, participants tracking, and trial data analysis. There remains however a strong impetus to integrate these diverse applications -each supporting different but related functions of clinical trial management -at syntactic and semantic levels so as to improve clarity, consistency and correctness in specifying clinical trials, and in acquiring and analyzing clinical data. The situation becomes especially critical with the need to manage multiple clinical trials at various sites, and to facilitate meta-analyses on trials. This paper introduces a knowledge-based framework that we are building to support a suite of clinical trial management applications. Our initiative uses semantic technologies to provide a consistent basis for the applications to interoperate. We are adapting this approach to the Immune Tolerance Network (ITN), an international research consortium developing new therapeutics in immune-mediated disorders.

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Ontology-Centered Syndromic Surveillance for Bioterrorism

Cited by 43 publications

References 8 publications

A workflow for software development within computational epidemiology

A workflow for software development within computational epidemiology

Supporting global health goals with information and communications technology

Towards Semantic Interoperability in a Clinical Trials Management System

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