Senior Design Projects
ECS193 A/B Winter & Spring 2020
Risk predictability of atrial fibrillation
| unsrivatsa@ucdavis.edu | |
| Name | Uma N Srivatsa MD |
| Affiliation | School of Medicine |
Project's details
| Project title | Risk predictability of atrial fibrillation |
| Background | 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. |
| Description | 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. |
| Deliverable | machine learning algorithm to identify risk of atrial fibrillation |
| Skill set desirable | Machine learning programming skills. EMR programming skills ( Sequel) |
| Phone number | 916 524 6267 |
| Client time availability | 30-60 min weekly or more |
| IP requirement | Open source project |
| Attachment | N/A |
| Selected | N/A |
| Stuff | N/A |
| TA | N/A |