title: Network analysis of toxic chemicals and symptoms: implications for designing first-responder systems.
abstract: The rapid and accurate identification of toxic chemicals is critical for saving lives in emergency situations. However, first-responder systems such as WISER typically require a large number of inputs before a chemical can be identified. To address this problem, we used networks to visualize and analyze the complex relationship between toxic chemicals and their symptoms. The results explain why current approaches require a large number of inputs and help to identify regularities related to the co-occurrence of symptoms. This understanding provides implications for the design of future first-responder systems, with the goal of rapidly identifying toxic chemicals in emergency situations.
authors.affiliation: School of Information, University of Michigan, USA.
authors.forename: Suresh K
authors.initials: SK
authors.lastname: Bhavnani
authors.forename: Annie
authors.initials: A
authors.lastname: Abraham
authors.forename: Christopher
authors.initials: C
authors.lastname: Demeniuk
authors.forename: Messeret
authors.initials: M
authors.lastname: Gebrekristos
authors.forename: Abe
authors.initials: A
authors.lastname: Gong
authors.forename: Satyendra
authors.initials: S
authors.lastname: Nainwal
authors.forename: Gautam K
authors.initials: GK
authors.lastname: Vallabha
authors.forename: Rudy J
authors.initials: RJ
authors.lastname: Richardson
mesh_terms: D000465:Algorithms; D001296:Audiovisual Aids; D019992:Databases as Topic; D003661:Decision Support Techniques; D004631:Emergency Medical Service Communication Systems; D004632:Emergency Medical Services; D015386:Hazardous Substances / Q000009:adverse effects*; D006801:Humans
chemical_list: D015386:Hazardous Substances