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A retrospective study on machine learning-assisted stroke recognition for medical helpline calls.
Wenstrup, Jonathan; Havtorn, Jakob Drachmann; Borgholt, Lasse; Blomberg, Stig Nikolaj; Maaloe, Lars; Sayre, Michael R; Christensen, Hanne; Kruuse, Christina; Christensen, Helle Collatz.
Afiliación
  • Wenstrup J; Department of Neurology, Copenhagen University Hospital, Herlev and Gentofte, Borgmester Ib Juuls Vej 1, 2730, Herlev, Denmark.
  • Havtorn JD; Copenhagen Emergency Medical Services, Telegrafvej 5, 2750, Ballerup, Denmark.
  • Borgholt L; Corti, Store Strandstræde 21, 1255, Copenhagen, Denmark.
  • Blomberg SN; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, 321, 223, 2800 Kgs, Lyngby, Denmark.
  • Maaloe L; Corti, Store Strandstræde 21, 1255, Copenhagen, Denmark.
  • Sayre MR; Department of Electronic Systems, Aalborg University, Fredrik Bajers Vej 7K, 9220, Aalborg Ø, Denmark.
  • Christensen H; Pioneer Centre for Artificial Intelligence, Øster Voldgade 3, 1350, Copenhagen, Denmark.
  • Kruuse C; Prehospital Centre Region Zealand, Ringstedgade 61, 4700, Næstved, Denmark.
  • Christensen HC; Corti, Store Strandstræde 21, 1255, Copenhagen, Denmark.
NPJ Digit Med ; 6(1): 235, 2023 Dec 19.
Article en En | MEDLINE | ID: mdl-38114611
ABSTRACT
Advanced stroke treatment is time-dependent and, therefore, relies on recognition by call-takers at prehospital telehealth services to ensure fast hospitalisation. This study aims to develop and assess the potential of machine learning in improving prehospital stroke recognition during medical helpline calls. We used calls from 1 January 2015 to 31 December 2020 in Copenhagen to develop a machine learning-based classification pipeline. Calls from 2021 are used for testing. Calls are first transcribed using an automatic speech recognition model and then categorised as stroke or non-stroke using a text classification model. Call-takers achieve a sensitivity of 52.7% (95% confidence interval 49.2-56.4%) with a positive predictive value (PPV) of 17.1% (15.5-18.6%). The machine learning framework performs significantly better (p < 0.0001) with a sensitivity of 63.0% (62.0-64.1%) and a PPV of 24.9% (24.3-25.5%). Thus, a machine learning framework for recognising stroke in prehospital medical helpline calls may become a supportive tool for call-takers, aiding in early and accurate stroke recognition.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: NPJ Digit Med Año: 2023 Tipo del documento: Article País de afiliación: Dinamarca Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: NPJ Digit Med Año: 2023 Tipo del documento: Article País de afiliación: Dinamarca Pais de publicación: Reino Unido