A retrospective study on machine learning-assisted stroke recognition for medical helpline calls

Author:

Wenstrup JonathanORCID,Havtorn Jakob DrachmannORCID,Borgholt LasseORCID,Blomberg Stig Nikolaj,Maaloe LarsORCID,Sayre Michael R.ORCID,Christensen HanneORCID,Kruuse Christina,Christensen Helle CollatzORCID

Abstract

AbstractAdvanced 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.

Funder

TrygFonden

Herlev Hospital

Københavns Universitet

Innovationsfonden

Novo Nordisk Fonden

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Computer Science Applications,Medicine (miscellaneous)

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