GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response

EMS (emergency medical service) plays an important role in saving lives in emergency and accident situations. When first responders, including EMS providers and firefighters, arrive at an incident, they communicate with the patients (if conscious), family members and other witnesses, other first responders, and the command center. The first responders utilize a microphone and headset to support these communications. After the incident, the first responders are required to document the incident by filling out a form. Today, this is performed manually. Manual documentation of patient summary report is time-consuming, tedious, and error-prone. We have addressed these form filling problems by transcribing the audio from the scene, identifying the relevant information from all the conversations, and automatically filling out the form. Informal survey of first responders indicate that this application would be exceedingly helpful to them. Results show that we can fill out a model summary report form with an F1 score as high as 94%, 78%, 96%, and 83% when the data is noise-free audio, noisy audio, noise-free textual narratives, and noisy textual narratives, respectively.

[1]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[2]  Varol Akman,et al.  Current approaches to punctuation in computational linguistics , 1996, Comput. Humanit..

[3]  Mahadev Satyanarayanan,et al.  Towards wearable cognitive assistance , 2014, MobiSys.

[4]  Tanel Alumäe,et al.  Bidirectional Recurrent Neural Network with Attention Mechanism for Punctuation Restoration , 2016, INTERSPEECH.

[5]  Hongfang Liu,et al.  CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines , 2017, J. Am. Medical Informatics Assoc..

[6]  Wendy W. Chapman,et al.  Evaluation of negation phrases in narrative clinical reports , 2001, AMIA.

[7]  John A. Stankovic,et al.  Towards a Cognitive Assistant System for Emergency Response , 2018, 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS).

[8]  Vamsi Krishna,et al.  Identifying Semantics in Clinical Reports Using Neural Machine Translation , 2019, AAAI.

[9]  Homa Alemzadeh,et al.  A Behavior Tree Cognitive Assistant System for Emergency Medical Services , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[10]  Hongfang Liu,et al.  DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx , 2015, J. Biomed. Informatics.

[11]  Daniel Jurafsky,et al.  Parsing to Stanford Dependencies: Trade-offs between Speed and Accuracy , 2010, LREC.

[12]  Sunghwan Sohn,et al.  Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications , 2010, J. Am. Medical Informatics Assoc..

[13]  Yulan He,et al.  Extracting Topical Phrases from Clinical Documents , 2016, AAAI.

[14]  Bryony Dean Franklin,et al.  Missing Clinical Information in NHS hospital outpatient clinics: prevalence, causes and effects on patient care , 2011, BMC health services research.

[15]  Alan R. Aronson,et al.  Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program , 2001, AMIA.

[16]  Shourya Roy,et al.  How Much Noise Is Too Much: A Study in Automatic Text Classification , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[17]  David W. Embley,et al.  Ontology-Based Information Extraction with a Cognitive Agent , 2015, AAAI.

[18]  Alessandro Ricci,et al.  Real-time tracking and documentation in trauma management , 2019, Health Informatics J..