Hybrid decision support to monitor atrial fibrillation for stroke prevention

• Main Authors: Lei, Ningrong, Kareem, Murtadha, Moon, Seung Ki, Ciaccio, Edward J., Acharya, U. Rajendra, Faust, Oliver
• Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
• Format: Article
• Language: English
• Published: 2021
• Subjects: Science::Medicine; Human and AI Collaboration; Medical Diagnosis Support
• Online Access: https://hdl.handle.net/10356/146404
• Institution: Nanyang Technological University

In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis.