Python package for managing OHDSI clinical data models. Includes support for LLM based plain text queries!

Personal Health Data Analysis in Jupyter Notebook

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

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.

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

State-Level Risk Factor Analysis of Mental Health Wellbeing

Machine learning diabetes prediction mini project

Introduction to Data Collection Methods(IDCM)

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.

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.

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.

Simple IMC Calculator built in Flutter