EMSContExt: EMS Protocol-Driven Concept Extraction for Cognitive Assistance in Emergency Response

Abstract

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.

Large-Scale Multi-granular Concept Extraction Based on Machine Reading Comprehension

“…Knowledge-based Method These methods [27,4,26] mainly use the external information from KGs to achieve extraction tasks, resulting in good CE. However, these models have poor generalization ability and only focus on a specific field.…”

A comparative analysis for spatio-temporal spreading patterns of emergency news

“…Twitter, in particular, has been used to offer a clearer picture of what is currently happening or has happened at the scene of a disaster, even when most other communication networks are no longer available. Moreover, the open and timely circulation of emergency event information is vital for many crucial services, such as event detection 10,11 , disaster prediction and warning 12 , emergency response 13,14 , the evaluation and ranking of risk severity, resource controls and reaction planning 15 , reporting and monitoring of risk performance, and reviewing risk management 16 frameworks.…”