Popular GitHub Repositories for Menstrual Health

OpenHealth, AI Health Assistant | Powered by Your Data

Query your Apple Health data with natural language ? ?

心理健康大模型、LLM、The Big Model of Mental Health、Finetune、InternLM2、InternLM2.5、Qwen、ChatGLM、Baichuan、DeepSeek、Mixtral、LLama3、GLM4、Qwen2、LLama3.1

rPPG-Toolbox: Deep Remote PPG Toolbox (NeurIPS 2023)

Medical Q&A with Deep Language Models

This extension makes vscode seamlessly work with dbt?: Auto-complete, preview, column lineage, AI docs generation, health checks, cost estimation etc

This repository contains the code to generate predictions of critical violations at food establishments in Chicago. It also contains the results of an evaluation of the effectiveness of those predictions.

智能故障诊断和寿命预测期刊（Journals of Intelligent Fault Diagnosis and Remaining Useful Life）

A vision-language foundation model for computational pathology - Nature Medicine

A framework for creating grounded instruction based datasets and training conversational domain expert Large Language Models (LLMs).

It is a medical chatbot that will provide quick answers to FAQs by setting up rule-based keyword chatbots.

ECG arrhythmia classification using a 2-D convolutional neural network

Multimodal Question Answering in the Medical Domain: A summary of Existing Datasets and Systems

HI-ML toolbox for deep learning for medical imaging and Azure integration

This repository introduces MentaLLaMA, the first open-source instruction following large language model for interpretable mental health analysis.

Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"

Finetuning of Falcon-7B LLM using QLoRA on Mental Health Conversational Dataset

? Detection of restorations and treatments on dental x-rays in Tensorflow, using Faster-RCNN

Officially Accepted to IEEE Transactions on Medical Imaging (TMI, IF: 11.037) - Special Issue on Geometric Deep Learning in Medical Imaging.

In this project, the objective is to predict whether the person has Diabetes or not based on various features like Number of Pregnancies, Insulin Level, Age, BMI.