The NHANES Data 'API' is a Python tool that simplifies access to the National Health and Nutrition Examination Survey (NHANES) dataset. This project provides an easy-to-use API to retrieve NHANES data, helping researchers, data scientists, health professionals, and other stakeholders access these valuable datasets.

Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.

This course equips professionals to apply data science in addressing public health and healthcare challenges. It covers data handling, statistical analysis, machine learning, epidemiology, and health informatics, with a focus on real-world applications. Graduates are prepared for impactful roles across the health sector.

My work on the Fundamental Principles in Data Science Master in Univeristat de Barcelona

Multi-source public health intelligence system for epidemiological surveillance and early warning

Acesso eficiente e reprodutível aos microdados do DATASUS em R, com suporte a filtros e seleção de colunas.

🚨 Detect disease outbreaks in real-time with the Argus Platform, a public health intelligence system integrating clinical, social, and environmental data.

Health Data Science projects from IBM Professional Certificate

This repository contains work that has been carried out when completing the VOICES project. Generally, it has supplementary material demonstrating the analysis used in various research projects within VOICES.

Exploratory biomechanical study using ML to detect orthopedic bracing patterns in gait. Analysis of kinematic chain coupling, ROM restriction, and a methodological review of subject leakage in small datasets.

Short information about this GitHub profile

Analysis and visualization of Continuous Glucose Monitoring (CGM) data with Python.

Malaria Screener 🦟🔎🤖🧠

Analysis and visualization of Blood Glucose Monitoring (BGM) data with Python.

Real-time Clinical Decision Support System (CDSS) for detecting Adverse Drug Events (ADEs) using XGBoost, Streamlit, and Generative AI. Designed to automate pharmacovigilance workflows.

Leveraging AI to transform FDA FAERS data into actionable pharmaceutical brand strategies and safety monitoring insights.

Study plan for learning Health Data Science(HDS)

Analyzing patient care pathways using EHR data for chronic disease management

Full-stack AI-powered health analytics platform combining Oura biometric data with custom tag-based pattern analysis, enabling detection of relationships (e.g., symptoms, lifestyle factors) that are not accessible in the native Oura app.