Ulster University Logo

A Decision Support System and Rule-based Algorithm to Augment the Human Interpretation of the 12-lead Electrocardiogram

Cairns, Andrew, Bond, Raymond, Finlay, Dewar, Guldenring, Daniel, Badilini, Fabio, Libretti, Guido, Peace, Aaron and Leslie, Stephen (2017) A Decision Support System and Rule-based Algorithm to Augment the Human Interpretation of the 12-lead Electrocardiogram. Journal of Electrocardiology, 50 (6). pp. 781-786. [Journal article]

[img] Text - Supplemental Material
Restricted to Repository staff only

[img] Text - Accepted Version

URL: http://www.sciencedirect.com/science/article/pii/S0022073617302479

DOI: 10.1016/j.jelectrocard.2017.08.007


BackgroundThe 12-lead Electrocardiogram (ECG) has been used to detect cardiac abnormalities in the same format for more than 70 years. However, due to the complex nature of 12-lead ECG interpretation, there is a significant cognitive workload required from the interpreter. This complexity in ECG interpretation often leads to errors in diagnosis and subsequent treatment. We have previously reported on the development of an ECG interpretation support system designed to augment the human interpretation process. This computerised decision support system has been named ‘Interactive Progressive based Interpretation’ (IPI). In this study, a decision support algorithm was built into the IPI system to suggest potential diagnoses based on the interpreter’s annotations of the 12-lead ECG. We hypothesise semi-automatic interpretation using a digital assistant can be an optimal man-machine model for ECG interpretation.Objectives: To improve interpretation accuracy and reduce missed co-abnormalities.Methods: The Differential Diagnoses Algorithm (DDA) was developed using web technologies where diagnostic ECG criteria are defined in an open storage format, Javascript Object Notation (JSON), which is queried using a rule-based reasoning algorithm to suggest diagnoses. To test our hypothesis, a counterbalanced trial was designed where subjects interpreted ECGs using the conventional approach and using the IPI + DDA approach.ResultsA total of 375 interpretations were collected. The IPI + DDA approach was shown to improve diagnostic accuracy by 8.7% (although not statistically significant, p-value = 0.1852), the IPI + DDA suggested the correct interpretation more often than the human interpreter in 7/10 cases (varying statistical significance). Human interpretation accuracy increased to 70% when seven suggestions were generated.ConclusionAlthough results were not found to be statistically significant, we found; 1) our decision support tool increased the number of correct interpretations, 2) the DDA algorithm suggested the correct interpretation more often than humans, and 3) as many as 7 computerized diagnostic suggestions augmented human decision making in ECG interpretation. Statistical significance may be achieved by expanding sample size.

Item Type:Journal article
Keywords:ECG, Decision support, Rule based algorithms, CDSS, DSS. HCI
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > School of Engineering
Research Institutes and Groups:Engineering Research Institute
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:38511
Deposited By: Dr Raymond Bond
Deposited On:28 Aug 2017 09:15
Last Modified:09 Aug 2018 22:23

Repository Staff Only: item control page