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.
Algorithms Audiovisual Aids