Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework
| Autor: | Venkatesh Avula, Kyle Marshall, Vida Abedi, Chadd K. Kraus, Ramin Zand, Nayan Chaudhary, Debdipto Misra, Durgesh Chaudhary, Ayesha Khan, Jiang Li, Xiao Li, Fabien Scalzo, Clemens M. Schirmer, Dhruv Mathrawala |
|---|---|
| Přispěvatelé: | Fralin Life Sciences Institute |
| Rok vydání: | 2020 |
| Předmět: |
Decision support system
acute stroke Process (engineering) Review Health records stroke diagnosis lcsh:RC346-429 cerebrovascular disease/stroke 03 medical and health sciences 0302 clinical medicine Machine learning ischemic stroke Medicine 030212 general & internal medicine Severe disability Stroke lcsh:Neurology. Diseases of the nervous system Cause of death Pharmacology computer aided diagnosis business.industry Treatment delay Emergency department artificial intelligence medicine.disease stroke cerebrovascular disease stroke in emergency department machine learning Neurology Neurology (clinical) Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Therapeutic Advances in Neurological Disorders, Vol 13 (2020) Therapeutic Advances in Neurological Disorders |
| ISSN: | 1756-2864 |
| DOI: | 10.1177/1756286420938962 |
| Popis: | 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. Geisinger Health Plan Quality Fund; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R56HL116832] This work was sponsored in part by funds from the Geisinger Health Plan Quality Fund and National Institute of Health R56HL116832 (subaward) to VA and RZ. The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|