Abstract
BackgroundEach year cardiac arrest kills 60,000 people in the UK [1-2]. The use of an automatic external defibrillator (AED) in the first few minutes can increase the probability of survival from less than 5% to over 75%. However, there is a challenge to build AEDs that are ‘usable’ to all members of the public. This study investigated if we could predict whether a profiled bystander is likely to succeed in delivering a shock. MethodsA total of 362 subjects were recruited at a shopping mall and were asked to use an AED in a simulated emergency situation as facilitated by a ‘sensorised’ mannequin. During this we extracted a range of features (i.e. Age, Gender, Education, CPR/AED Training, Time-to-Place-Electrodes, Time-to-First-Shock). Using logistic regression, Odds ratios (ORs) were analysed to identify variables that decrease/increase the likelihood of delivering a shock. The dataset was also split into training (70%) and testing datasets (30%) to build and evaluate an ensemble C5.0 decision tree to predict if a person is likely to deliver a successful shock. ResultsThe variables (1) Time-to-First-Shock [OR= 8.97], (2) Prior Defibrillation Training [OR= 8.37], (3) Prior CPR Training [OR= 6.76], High school education [OR= 5.71]) have the largest ORs. This may indicate that users with these characteristics are likely to deliver a shock. However, no OR had statistical significance (p
Original language | English |
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Title of host publication | Unknown Host Publication |
Publisher | C-TRIC |
Number of pages | 1 |
Publication status | Published (in print/issue) - 27 Oct 2015 |
Event | 7th Annual Translational Medicine Conference - Derry/Londonderry Duration: 27 Oct 2015 → … |
Conference
Conference | 7th Annual Translational Medicine Conference |
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Period | 27/10/15 → … |
Keywords
- machine learning
- health informatics
- human computer interaction