Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention.
Ningrong LeiMurtadha KareemSeung Ki MoonEdward J CiaccioUdyavara Rajendra AcharyaOliver FaustPublished in: International journal of environmental research and public health (2021)
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.
Keyphrases
- atrial fibrillation
- deep learning
- healthcare
- oral anticoagulants
- catheter ablation
- left atrial
- left atrial appendage
- machine learning
- direct oral anticoagulants
- primary care
- heart rate
- heart failure
- percutaneous coronary intervention
- mental health
- endothelial cells
- blood pressure
- emergency department
- high throughput
- heart rate variability
- acute coronary syndrome
- subarachnoid hemorrhage
- health information
- blood brain barrier
- social media
- venous thromboembolism
- real time pcr
- cerebral ischemia