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

Research output: Contribution to journal › Journal article › Research › peer-review

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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.

In: npj Digital Medicine, Vol. 6, No. 1, 235, 2023.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Wenstrup, J, Havtorn, JD, Borgholt, L, Blomberg, SN, Maaloe, L, Sayre, MR, Christensen, H, Kruuse, C & Christensen, HC 2023, 'A retrospective study on machine learning-assisted stroke recognition for medical helpline calls', npj Digital Medicine, vol. 6, no. 1, 235. https://doi.org/10.1038/s41746-023-00980-y

APA

Wenstrup, J., Havtorn, J. D., Borgholt, L., Blomberg, S. N., Maaloe, L., Sayre, M. R., Christensen, H., Kruuse, C., & Christensen, H. C. (2023). A retrospective study on machine learning-assisted stroke recognition for medical helpline calls. npj Digital Medicine, 6(1), [235]. https://doi.org/10.1038/s41746-023-00980-y

Vancouver

Wenstrup J, Havtorn JD, Borgholt L, Blomberg SN, Maaloe L, Sayre MR et al. A retrospective study on machine learning-assisted stroke recognition for medical helpline calls. npj Digital Medicine. 2023;6(1). 235. https://doi.org/10.1038/s41746-023-00980-y

Author

Wenstrup, Jonathan ; Havtorn, Jakob Drachmann ; Borgholt, Lasse ; Blomberg, Stig Nikolaj ; Maaloe, Lars ; Sayre, Michael R. ; Christensen, Hanne ; Kruuse, Christina ; Christensen, Helle Collatz. / A retrospective study on machine learning-assisted stroke recognition for medical helpline calls. In: npj Digital Medicine. 2023 ; Vol. 6, No. 1.

Bibtex

@article{6e0e6bca8d154be8a6b707a82eb79778,

title = "A retrospective study on machine learning-assisted stroke recognition for medical helpline calls",

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.",

author = "Jonathan Wenstrup and Havtorn, {Jakob Drachmann} and Lasse Borgholt and Blomberg, {Stig Nikolaj} and Lars Maaloe and Sayre, {Michael R.} and Hanne Christensen and Christina Kruuse and Christensen, {Helle Collatz}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s).",

year = "2023",

doi = "10.1038/s41746-023-00980-y",

language = "English",

volume = "6",

journal = "npj Digital Medicine",

issn = "2398-6352",

publisher = "Nature Publishing Group",

number = "1",

}

RIS

TY - JOUR

T1 - A retrospective study on machine learning-assisted stroke recognition for medical helpline calls

AU - Wenstrup, Jonathan

AU - Havtorn, Jakob Drachmann

AU - Borgholt, Lasse

AU - Blomberg, Stig Nikolaj

AU - Maaloe, Lars

AU - Sayre, Michael R.

AU - Christensen, Hanne

AU - Kruuse, Christina

AU - Christensen, Helle Collatz

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

U2 - 10.1038/s41746-023-00980-y

DO - 10.1038/s41746-023-00980-y

M3 - Journal article

AN - SCOPUS:85180128484

VL - 6

JO - npj Digital Medicine

JF - npj Digital Medicine

SN - 2398-6352

IS - 1

M1 - 235

ER -

ID: 377807167