Leveraging Data Science to improve patient outcomes. This portfolio showcases my work in Medical AI, ranging from 1st-place hackathon solutions in public health management to top-10 finishes in complex radiological imaging challenges.
8th Place (Team: ML&Etc)
- Developed a Machine Learning model to accurately predict mammography recall rates, addressing the challenge of reducing false positives in breast cancer screening.
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10th Place (Team: Lidera Health)
- Engineered a Deep Learning model capable of predicting patient age groups based on Head CT scans, demonstrating robust feature extraction from volumetric medical imaging.
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Scientific Initiation | Hospital Israelita Albert Einstein (HIAE)
- Conducted research to predict brain age groups using Head CTs. This project laid the foundation for my expertise in neuroimaging analysis.
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Computer Vision Project
- Applied Deep Learning techniques (CNNs) to detect COVID-19 patterns in chest X-ray images, contributing to rapid-response tools during the pandemic.
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Predictive Modeling
- Built a triage model using clinical data to predict COVID-19 diagnosis, aiming to assist healthcare facilities in resource allocation and patient management.
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1st Place Winner
- Created a predictive model for medical appointment absenteeism (no-shows). The solution focused on optimizing public health scheduling and reducing idle resources.
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Finalist
- Developed a risk stratification model to predict severe asthma cases, aiming to improve preventive care strategies for high-risk patients.
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Public Health Data Challenge
- Analyzed public health data to predict infant mortality rates, providing insights to support data-driven government interventions.
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