Python package for managing OHDSI clinical data models. Includes support for LLM based plain text queries, MCP server and FHIR import.
Track and visualize your blood test results over time with AI-powered data extraction
Python-based machine learning and data science module from SFSU developed for the NIGMS Sandbox project
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.
Secure, local-first archive for Garmin Connect health data (HRV, sleep, Body Battery, activities). Private & offline. Windows desktop app, no setup needed. Structured for local analysis (Excel, HTML dashboards, JSON for Ollama/Open WebUI). Save your data from decay.
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.
An application for creating, validating, reusing and extending sets of clinical codes.
MusicPsychologyToolbox
VetSalud Database- Complete PostgreSQL solution for veterinary clinic management. Features: patient tracking, appointment scheduling, medical records, inventory control, audit trails, and business intelligence views. Production-ready.
Live births in Scotland 2021 - Exercise on spatial vector data with R - RMarkdown file
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.
Objective, create an intuitive and user-friendly web-based application for visualizing and exploring NHANES data. This dashboard will enable users, including those with limited or no Python programming experience, to interact with NHANES data and generate informative visualizations to gain insights into various health-related aspects.
Presentation about the "self-controlled case series (SCCS)" method.
Machine learning diabetes prediction mini project
This project implements a decision tree model built from scratch without using any ML libraries/frameworks to classify patients as either containing diabetic retinopathy or not.
Python data analytics project analysing global health indicators using EDA, regression, and clustering.
Machine learning project predicting sleep quality using Linear Regression, Random Forest, and Neural Networks with TensorFlow/Keras. Includes tutorial notes and full analysis.
Statistical analysis of birthweight and related predictors using Python
State-Level Risk Factor Analysis of Mental Health Wellbeing
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