Taking Behavioral Science to the next Level: Opportunities for the Use of Ontologies to Enable Artificial Intelligence-Driven Evidence Synthesis and Prediction
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Author / Producer
Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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Abstract
Decades of research have created a vast archive of information on human behavior, with relevant new studies being published daily. Despite these advances, knowledge generated by behavioral science – the social and biological sciences concerned with the study of human behavior – is not efficiently translated for those who will apply it to benefit individuals and society. The gap between what is known and the capacity to act on that knowledge continues to widen as current evidence synthesis methods struggle to process a large, ever-growing body of evidence characterized by its complexity and lack of shared terminologies. The purpose of the present position paper is twofold: (i) to highlight the pitfalls of traditional evidence synthesis methods in supporting effective knowledge translation to applied settings, and (ii) to sketch a potential alternative evidence synthesis approach which leverages on the use of ontologies – formal systems for organizing knowledge – to enable a more effec tive, artificial intelligence-driven accumulation and implementation of knowledge. The paper concludes with future research directions across behavioral, computer, and information sciences to help realize such innovative approach to evidence synthesis, allowing behavioral science to advance at a faster pace.
Permanent link
Publication status
published
External links
Book title
Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: Scale-IT-up
Journal / series
Volume
Pages / Article No.
641 - 678
Publisher
SciTePress
Event
17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Taxonomy; Classification system; Ontology; Behavioral medicine; Health psychology; Evidence synthesis; Systematic review; Machine learning
Organisational unit
03995 - von Wangenheim, Florian / von Wangenheim, Florian
03681 - Fleisch, Elgar / Fleisch, Elgar
02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.