Senior Design Projects
ECS193 A/B Winter & Spring 2020
Risk predictability of atrial fibrillation
| ********** | |
| Uma N Srivatsa MD | |
| School of Medicine |
Project's details
| Risk predictability of atrial fibrillation | |
| Premature atrial complexes (PAC) frequently occur in 24 hour electrocardiogram ( ECG) monitor. There are precursor to a condition called atrial fibrillation which carries a risk of stroke. | |
| We identify all patients with Holter monitor between years 2010 and 2018. Two groups - those with and those without atrial fibrillation. We collect clinical. demographic data from electronic medical record. In addition. parameters from ECG monitor would be heart rate and PAC characteristics: number of PAC. morphology of PAC. normal complex to PAC interval. Using all these parameters we need to identify a machine learning algorithm to predict occurence of atrial fibrillaiton. One set up of patients will be to program algorithm. and second set of patients will be to validate. | |
| machine learning algorithm to identify risk of atrial fibrillation | |
| Machine learning programming skills. EMR programming skills ( Sequel) | |
| ********** | |
| 30-60 min weekly or more | |
| Open source project | |
| Attachment | N/A |
| No | |
| Team members | N/A |
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