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. 2023 Dec 6;2(12):e0000406.
doi: 10.1371/journal.pdig.0000406. eCollection 2023 Dec.

AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal

Dalton Breno Costa  1 , Felipe Coelho de Abreu Pinna  2 , Anjni Patel Joiner  3   4 , Brian Rice  5 , João Vítor Perez de Souza  3   4 , Júlia Loverde Gabella  6 , Luciano Andrade  6 , João Ricardo Nickenig Vissoci  3   4 , João Carlos Néto  2
Affiliations

AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal

Dalton Breno Costa et al. PLOS Digit Health. .

Abstract

Emergency care-sensitive conditions (ECSCs) require rapid identification and treatment and are responsible for over half of all deaths worldwide. Prehospital emergency care (PEC) can provide rapid treatment and access to definitive care for many ECSCs and can reduce mortality in several different settings. The objective of this study is to propose a method for using artificial intelligence (AI) and machine learning (ML) to transcribe audio, extract, and classify unstructured emergency call data in the Serviço de Atendimento Móvel de Urgência (SAMU) system in southern Brazil. The study used all "1-9-2" calls received in 2019 by the SAMU Novo Norte Emergency Regulation Center (ERC) call center in Maringá, in the Brazilian state of Paraná. The calls were processed through a pipeline using machine learning algorithms, including Automatic Speech Recognition (ASR) models for transcription of audio calls in Portuguese, and a Natural Language Understanding (NLU) classification model. The pipeline was trained and validated using a dataset of labeled calls, which were manually classified by medical students using LabelStudio. The results showed that the AI model was able to accurately transcribe the audio with a Word Error Rate of 42.12% using Wav2Vec 2.0 for ASR transcription of audio calls in Portuguese. Additionally, the NLU classification model had an accuracy of 73.9% in classifying the calls into different categories in a validation subset. The study found that using AI to categorize emergency calls in low- and middle-income countries is largely unexplored, and the applicability of conventional open-source ML models trained on English language datasets is unclear for non-English speaking countries. The study concludes that AI can be used to transcribe audio and extract and classify unstructured emergency call data in an emergency system in southern Brazil as an initial step towards developing a decision-making support tool.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Proposed methodology overview diagram.
Fig 2
Fig 2. Annotation of recorded call.
Fig 3
Fig 3. Sample NLU application for emergency domain.
Fig 4
Fig 4. Confusion matrix for the NLU emergency type classification model validation.
Fig 5
Fig 5. ROC curves for the NLU emergency type classification.
See this image and copyright information in PMC

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