EMSContExt: EMS Protocol-Driven Concept Extraction for Cognitive Assistance in Emergency Response - Preum, Sarah Masud; Shu, Sile; Alemzadeh, Homa; Stankovic, John A. (Proceedings of the AAAI Conference on Artificial Intelligence, 2020)
This paper presents a technique for automated curation of a domain-specific knowledge base or lexicon for resource-constrained domains, such as Emergency Medical Services (EMS) and its application to real-time concept extraction and cognitive assistance in emergency response. The EMS responders often verbalize critical information describing the situations at an incident scene, including patients' physical condition and medical history. Automated extraction of EMS protocol-specific concepts from responders' speech data can facilitate cognitive support through the selection and execution of the proper EMS protocols for patient treatment. Although this task is similar to the traditional NLP task of concept extraction, the underlying application domain poses major challenges, including low training resources availability (e.g., no existing EMS ontology, lexicon, or annotated EMS corpus) and domain mismatch. Hence, we develop EMSContExt, a weakly-supervised concept extraction approach for EMS concepts. It utilizes different knowledge bases and a semantic concept model based on a corpus of over 9400 EMS narratives for lexicon expansion. The expanded EMS lexicon is then used to automatically extract critical EMS protocol-specific concepts from real-time EMS speech narratives. Our experimental results show that EMSContExt achieves 0.85 recall and 0.82 F1-score for EMS concept extraction and significantly outperforms MetaMap, a state-of-the-art medical concept extraction tool. We also demonstrate the application of EMSContExt to EMS protocol selection and execution and real-time recommendation of protocol-specific interventions to the EMS responders. Here, EMSContExt outperforms MetaMap with a 6% increase and six times speedup in weighted recall and execution time, respectively.