Automated syndrome classification using early phase emergency department data

The primary motivation behind automated syndrome classification is to shorten the time it may take to detect an outbreak or a community-wide public health issue. State-of-the-art syndrome classification techniques have primarily used Emergency Department (ED) chief complaints (CCs) or other short free text descriptions of patient symptoms for near real-time surveillance purposes. We propose a system that can automatically classify an ED record with initial temperature, chief complaint and triage nurse's notes into one or more syndromes using the vector space model. Terms from syndrome definitions and a small tf-idf weighted syndrome-positive training set are used to create a reference dictionary for generating the syndrome and triage note vectors. Subsequently, cosine similarity between the vectors is used to establish the particular syndrome category. We tested the system on a manually classified dataset of 485 ED records for the gastro-intestinal (GI) syndrome and measured performance in terms of sensitivity (~93%) and specificity (~82%). Initial results show that this system is capable of producing a better balance between these measures compared to existing automatic syndrome categorization systems which use only chief complaints and/or diagnosis codes.