Automating hybrid collective intelligence in open-ended medical diagnostics.

ABSTRACT: Collective intelligence has emerged as a powerful mechanism to boost decision accuracy across many domains, such as geopolitical forecasting, investment, and medical diagnostics. However, collective intelligence has been mostly applied to relatively simple decision tasks (e.g., binary classifications). Applications in more open-ended tasks with a much larger problem space, such as emergency management or general medical diagnostics, are largely lacking, due to the challenge of integrating unstandardized inputs from different crowd members. Here, we present a fully automated approach for harnessing collective intelligence in the domain of general medical diagnostics. Our approach leverages semantic knowledge graphs, natural language processing, and the SNOMED CT medical ontology to overcome

SUBMITTER: Kurvers RHJM 

PROVIDER: S-EPMC10450668 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature