Python package for managing OHDSI clinical data models. Includes support for LLM based plain text queries, MCP server and FHIR import.
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Updated
Sep 7, 2026 - Python
Python package for managing OHDSI clinical data models. Includes support for LLM based plain text queries, MCP server and FHIR import.
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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.
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.
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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.
A comparative study applying a stochastic Pure Death Process, Cox Proportional Hazards model, and logistic regression to breast cancer survival data from 272 Netherlands patients. Includes simulations, survival analysis, model diagnostics, and evaluation of hazard rate prediction across methods.
Privacy-first Apple Health & Apple Watch data analyzer for Windows. Imports HealthKit ZIP/XML locally, builds personal baselines, and explains sleep, heart rate, HRV, activity, blood oxygen and wrist-temperature trends with cross-metric evidence. Tauri · Rust · React. | 知衡健康
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.
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