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AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study
Cardiology Department, Leiden University Medical Center, Leiden, Netherlands.ORCID iD: 0000-0002-9343-0717
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-0887-3979
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-8561-7963
Clinical AI and Research lab, Leiden University Medical Center, Leiden, Netherlands.ORCID iD: 0000-0001-5198-6269
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2023 (English)In: JMIR Cardio, E-ISSN 2561-1011, Vol. 7, no 1, article id e51375Article in journal (Refereed) Published
Abstract [en]

Background: Overcrowding of hospitals and emergency departments (EDs) is a growing problem. However, not all ED consultations are necessary. For example, 80% of patients in the ED with chest pain do not have an acute coronary syndrome (ACS). Artificial intelligence (AI) is useful in analyzing (medical) data, and might aid health care workers in prehospital clinical decision-making before patients are presented to the hospital.

Objective: The aim of this study was to develop an AI model which would be able to predict ACS before patients visit the ED. The model retrospectively analyzed prehospital data acquired by emergency medical services' nurse paramedics.

Methods: Patients presenting to the emergency medical services with symptoms suggestive of ACS between September 2018 and September 2020 were included. An AI model using a supervised text classification algorithm was developed to analyze data. Data were analyzed for all 7458 patients (mean 68, SD 15 years, 54% men). Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for control and intervention groups. At first, a machine learning (ML) algorithm (or model) was chosen; afterward, the features needed were selected and then the model was tested and improved using iterative evaluation and in a further step through hyperparameter tuning. Finally, a method was selected to explain the final AI model.

Results: The AI model had a specificity of 11% and a sensitivity of 99.5% whereas usual care had a specificity of 1% and a sensitivity of 99.5%. The PPV of the AI model was 15% and the NPV was 99%. The PPV of usual care was 13% and the NPV was 94%.

Conclusions: The AI model was able to predict ACS based on retrospective data from the prehospital setting. It led to an increase in specificity (from 1% to 11%) and NPV (from 94% to 99%) when compared to usual care, with a similar sensitivity. Due to the retrospective nature of this study and the singular focus on ACS it should be seen as a proof-of-concept. Other (possibly life-threatening) diagnoses were not analyzed. Future prospective validation is necessary before implementation.

Place, publisher, year, edition, pages
JMIR Publications, 2023. Vol. 7, no 1, article id e51375
National Category
Other Computer and Information Science
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-102492DOI: 10.2196/51375OAI: oai:DiVA.org:ltu-102492DiVA, id: diva2:1812980
Note

Validerad;2023;Nivå 1;2023-11-17 (joosat);

CC BY 4.0 License

Available from: 2023-11-17 Created: 2023-11-17 Last updated: 2023-11-17Bibliographically approved

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