Pronouns: He, Him, His
Research Mentor(s): Mohammed Saeed, Faculty Member / Clinical Lecturer
Research Mentor School/College/Department: Department of Internal Medicine, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 4 (2pm-2:50pm)
Breakout Room: Room 20
Massive amounts of data are being generated at a high velocity in the medical industry, at a rate and dimension that humans cannot catch up understanding them. Machine learning (ML) has the potential to analyze the medical time series data to support physicians in clinical decision making. However, in order to build useful machine learning (ML) algorithms, it is important to have annotated medical data set that can be used to train ML models. This project at the start is about building a viewer for surface ECG and intracardiac electrogram signals acquired during cardiac ablation procedures. Building the framework at early stages to support basic functionality is engineering-oriented with design decisions. We are currently developing a web application in Python. Specifications were created based upon the needed functionality of the application after talking with multiple cardiologists. The next step would be inviting medical students and cardiologists to use the app and gather feedback. Eventually, we want to build a platform/infrastructure that allows for development and evaluation of ML algorithms, and to improve cardiovascular disease management and treatment. By working with cardiologists and intelligent algorithms, we want to build a tool that can quickly review, search, annotate and analyze signals at high throughput. The end goal is that researchers across the country can use our tool to review their own data, and also deploy a real-time graphical decision support tool to assist cardiac procedures.