👋 Hi, my name is Charbel Marche (@charbelmarche33)!
🎓 I am a student physician and computer scientist completing my M.D. and M.S. in data science at the University of Virginia.
👀 I’m interested in applications of software engineering, AI, and machine learning in medicine, medical research, investing, and space tech.
🌱 I am a part of teams that are developing a plethora of tools, some of which I hope interest you:
| Project Name | Project Website | Description | Videos | Organization |
|---|---|---|---|---|
| NovaCards.AI | novacards.ai | 2022 UVA Entrepreneurship-Cup finalist & 2022 UVA I-Corps participant. NovaCards.ai is an ML and NLP-powered web app built by a team of student doctors to improve the efficiency and efficacy of studying. NovaCards.ai empowers users to create and find relevant flashcards quickly, create relevant practice questions, and other study materials based on lecture notes and materials. | Youtube Channel | NovaCards.ai GitHub Org |
| StockScrapers.com | stockscrapers.com | StockScrapers is the home of a collection of investing tools that empower retail investors to make informed decisions in the stock market. These tools allow users to do multiple things, such as simulating different investment strategies, easing financial modeling for any publicly traded corporation, and comparing historical financial statements for multiple companies. | Playlist of Demos | StockScrapers GitHub Org |
| Stock Financial Statement Retriever From SEC | API Home (RapidAPI) | The StockScrapers™ Stock Financial Statement Retriever API provides updated and complete access to 10-K financial statement records as reported to the SEC. Our API contains data for 7,171 tickers that report to the SEC organized by year and financial statement. The data provided includes all fields reported in the income statements, balance sheets, and cash flow statements dating back to 2003 as well as company names and URLs pointing to the sources for our data. | No Demos Yet | StockScrapers GitHub Org |
| IR Quality Improvement Dashboard | Not Publically Hosted | A fledgling quality improvement project with UVA Health's Interventional Radiology department. I have the privilege to collaborate with Jordan Bagnall, a fellow medical student, and many other of the wonderful staff at UVA Health under the guidance of Dr. John Angle to make this project a reality. Currently, the project shows adverse event frequencies, severity, and type for various IR procedures over 15 years at UVA for any specific patient demographics. The long-term vision for this project is to build a tool similar to the NSQIP calculator to predict complication risks for patients undergoing procedures using machine learning. | Short Video Demonstration | No Organization or Public Repo |
| ICD to AIS/ISS Medical Coding Converter | Not Publically Hosted | Abstract accepted into Society for Academic Emergency Medicine and presented at the SAEM 2023 conference. Additionally presented at the UVA Medical Student Research Symposium and MSSRP Summer Research Presentation Session in 2022 and the Research Computing Poster Exhibition in 2024. Worked with Dr. Hartka, an EM doctor and data scientist, to create and train various neural networks to predict appropriate abbreviated injury scale (AIS) codes for trauma patients based on the ICD codes given during hospital stays. Done with PyTorch. | Recorded Presentation | Repo |
| And more... |