Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework.
Vida AbediAyesha KhanDurgesh ChaudharyDebdipto MisraVenkatesh AvulaDhruv MathrawalaChadd KrausKyle A MarshallNayan ChaudharyXiao LiClemens M SchirmerFabien ScalzoJiang LiRamin ZandPublished in: Therapeutic advances in neurological disorders (2020)
Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients' presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.
Keyphrases
- artificial intelligence
- big data
- machine learning
- electronic health record
- atrial fibrillation
- deep learning
- emergency department
- end stage renal disease
- chronic kidney disease
- liver failure
- peritoneal dialysis
- cerebral ischemia
- clinical decision support
- adverse drug
- depressive symptoms
- prognostic factors
- patient reported outcomes
- respiratory failure
- case report
- decision making
- smoking cessation
- extracorporeal membrane oxygenation