Using Machine Learning to Predict if a Profiled Lay Rescuer can Successfully Deliver a Shock using a Public Access Automated External Defibrillator?

Raymond Bond, Hannah Torney, Peter O’Hare, Laura Davis, Bruno Delafont, Hannah McReynolds, Anna McLister, Ben McCartney, Rebecca Di Maio, Dewar Finlay, Daniel Guldenring, James McLaughlin, David McEneaney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A public access automated external defibrillator (AED) is a device that is intended to be used by lay rescuers in an event where a member of the public experiences a sudden cardiac arrest due to a severe ventricular arrhythmia. Therefore, it is imperative that the human-machine interface of an AED is optimized in terms of its usability and intuitive design. This study involved the recruitment of 362 subjects (lay people) in a shopping mall to undertake the task of using an AED in a simulated environment as facilitated by a ‘sensorised’ manikin and an AED that was developed by HeartSine Technologies. We found that a large proportion (91.44%) of lay people can successfully use an AED in a simulated emergency scenario to deliver a successful shock. We also found that CPR training did not provide greater likelihood for shock success whilst those with AED training did. Exploratory data analysis and machine learning were used to determine if demographics and other variables are potential predictors for delivering a successful shock using an AED. We found that user demographics and educational attainment were not predictive for AED ‘usage’ success, which is reassuring since the objective of the medical industry is to develop AEDs that are intuitive to any member of the public.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages1181-1184
Number of pages4
Volume43
Publication statusE-pub ahead of print - 2 Mar 2017
EventComputing in Cardiology - Vancouver
Duration: 2 Mar 2017 → …

Conference

ConferenceComputing in Cardiology
Period2/03/17 → …

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Defibrillators
Learning systems
Shopping centers

Keywords

  • AED
  • Automated External Defibrillator
  • machine learning
  • predictive modelling
  • human-machine systems
  • human computer interaction
  • health informatics

Cite this

Bond, R., Torney, H., O’Hare, P., Davis, L., Delafont, B., McReynolds, H., ... McEneaney, D. (2017). Using Machine Learning to Predict if a Profiled Lay Rescuer can Successfully Deliver a Shock using a Public Access Automated External Defibrillator? In Unknown Host Publication (Vol. 43, pp. 1181-1184)
Bond, Raymond ; Torney, Hannah ; O’Hare, Peter ; Davis, Laura ; Delafont, Bruno ; McReynolds, Hannah ; McLister, Anna ; McCartney, Ben ; Di Maio, Rebecca ; Finlay, Dewar ; Guldenring, Daniel ; McLaughlin, James ; McEneaney, David. / Using Machine Learning to Predict if a Profiled Lay Rescuer can Successfully Deliver a Shock using a Public Access Automated External Defibrillator?. Unknown Host Publication. Vol. 43 2017. pp. 1181-1184
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abstract = "A public access automated external defibrillator (AED) is a device that is intended to be used by lay rescuers in an event where a member of the public experiences a sudden cardiac arrest due to a severe ventricular arrhythmia. Therefore, it is imperative that the human-machine interface of an AED is optimized in terms of its usability and intuitive design. This study involved the recruitment of 362 subjects (lay people) in a shopping mall to undertake the task of using an AED in a simulated environment as facilitated by a ‘sensorised’ manikin and an AED that was developed by HeartSine Technologies. We found that a large proportion (91.44{\%}) of lay people can successfully use an AED in a simulated emergency scenario to deliver a successful shock. We also found that CPR training did not provide greater likelihood for shock success whilst those with AED training did. Exploratory data analysis and machine learning were used to determine if demographics and other variables are potential predictors for delivering a successful shock using an AED. We found that user demographics and educational attainment were not predictive for AED ‘usage’ success, which is reassuring since the objective of the medical industry is to develop AEDs that are intuitive to any member of the public.",
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Bond, R, Torney, H, O’Hare, P, Davis, L, Delafont, B, McReynolds, H, McLister, A, McCartney, B, Di Maio, R, Finlay, D, Guldenring, D, McLaughlin, J & McEneaney, D 2017, Using Machine Learning to Predict if a Profiled Lay Rescuer can Successfully Deliver a Shock using a Public Access Automated External Defibrillator? in Unknown Host Publication. vol. 43, pp. 1181-1184, Computing in Cardiology, 2/03/17.

Using Machine Learning to Predict if a Profiled Lay Rescuer can Successfully Deliver a Shock using a Public Access Automated External Defibrillator? / Bond, Raymond; Torney, Hannah; O’Hare, Peter; Davis, Laura; Delafont, Bruno; McReynolds, Hannah; McLister, Anna; McCartney, Ben; Di Maio, Rebecca; Finlay, Dewar; Guldenring, Daniel; McLaughlin, James; McEneaney, David.

Unknown Host Publication. Vol. 43 2017. p. 1181-1184.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - A public access automated external defibrillator (AED) is a device that is intended to be used by lay rescuers in an event where a member of the public experiences a sudden cardiac arrest due to a severe ventricular arrhythmia. Therefore, it is imperative that the human-machine interface of an AED is optimized in terms of its usability and intuitive design. This study involved the recruitment of 362 subjects (lay people) in a shopping mall to undertake the task of using an AED in a simulated environment as facilitated by a ‘sensorised’ manikin and an AED that was developed by HeartSine Technologies. We found that a large proportion (91.44%) of lay people can successfully use an AED in a simulated emergency scenario to deliver a successful shock. We also found that CPR training did not provide greater likelihood for shock success whilst those with AED training did. Exploratory data analysis and machine learning were used to determine if demographics and other variables are potential predictors for delivering a successful shock using an AED. We found that user demographics and educational attainment were not predictive for AED ‘usage’ success, which is reassuring since the objective of the medical industry is to develop AEDs that are intuitive to any member of the public.

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Bond R, Torney H, O’Hare P, Davis L, Delafont B, McReynolds H et al. Using Machine Learning to Predict if a Profiled Lay Rescuer can Successfully Deliver a Shock using a Public Access Automated External Defibrillator? In Unknown Host Publication. Vol. 43. 2017. p. 1181-1184