Python package for managing OHDSI clinical data models. Includes support for LLM based plain text queries, MCP server and FHIR import.

Personal Health Data Analysis in Jupyter Notebook

Track and visualize your blood test results over time with AI-powered data extraction

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.

Python-based machine learning and data science module from SFSU developed for the NIGMS Sandbox project

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

Live births in Scotland 2021 - Exercise on spatial vector data with R - RMarkdown file

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.

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.

Presentation about the "self-controlled case series (SCCS)" method.

Statistical analysis of birthweight and related predictors using Python

Machine learning project predicting sleep quality using Linear Regression, Random Forest, and Neural Networks with TensorFlow/Keras. Includes tutorial notes and full analysis.

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.

State-Level Risk Factor Analysis of Mental Health Wellbeing

Python data analytics project analysing global health indicators using EDA, regression, and clustering.

Healthcare Data Insights App This Streamlit application provides interactive visualizations and analyses of healthcare data. It includes modules for demographic, timeline, and treatment analysis, enabling users to explore patterns and gain insights from healthcare datasets.

Machine learning diabetes prediction mini project

Simple IMC Calculator built in Flutter

Analyzed heart attack risk in 10,000 Indian patients using MySQL and Python (Pandas, Matplotlib, Seaborn). Explored state-wise risk, lifestyle impacts, and income-health links. Found Mizoram had highest risk, Punjab highest cholesterol, and combined factors like smoking + obesity played key roles. Demonstrated EDA and data storytelling skills.

Contains two Machine Learning Projects on Health Data and Activity Recognition from an accelerometer

Introduction to Data Collection Methods(IDCM)

This project analyzes datasets to identify and visualize the most common diseases based on factors such as region, demographics, and time. Using statistical methods and data visualization tools (e.g., Python, Pandas, Matplotlib/Seaborn, or R), it provides insights into disease trends, helping healthcare professionals

A Collection of 120 Psychology Patients with 17 Essential Symptoms to Diagnose Mania Bipolar Disorder, Depressive Bipolar Disorder, Major Depressive Disorder, and Normal Individuals. The dataset contains the 17 essential symptoms psychiatrists use to diagnose the described disorders.

Learn how to create dynamic, AI-powered heatmap chart in WPF using Syncfusion SfHeat Map Chart to automatically highlight critical zones for public health insights.

Beginner-level data analysis on birthweight and maternal smoking using Python

Local-first health dashboard that displays CSV and JSON stored health data, as well as Oura ring metrics fetched from their API, on a pretty and useful dashboard.