Programming assignments, labs and quizzes from all courses in the Coursera AI for Medicine Specialization offered by deeplearning.ai

This is a reposotory that includes paper、code and datasets about domain generalization-based fault diagnosis and prognosis. (基于领域泛化的故障诊断和预测，持续更新)

This repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.

Data sets on prognosis and health management（PHM相关数据集）

Medical Images processing

Identification of heterogeneous biomarkers for breast cancer using Clustering and PageRank algorithms (bioinformatics)

deeplearning.ai - AI for Medicine Specialization (Andrew Ng, Pranav Rajpurkar)

AI application that can predict the survival of patients with heart failure using 12 clinical features.

This is the official repository of the R package metamisc

In this competition, we’ll predict a patient’s severity of decline in lung function based on a CT scan of their lungs. We’ll determine lung function based on output from a spirometer, which measures the volume of air inhaled and exhaled.

Classification and Segmentation: Diagnose diseases from x-rays and 3D MRI brain images. Predict patient survival rates more accurately using tree-based models. Estimate treatment effects on patients using data from randomized trials.

This repository contains the notes, codes, assignments, quizzes and other additional materials about the course "AI for Medical Prognosis" from DeepLearning.AI Coursera.

Machine Learning for the Visualization of Alzheimer's Disease

Classifying covid positive and negative cases in ct-scan images. Though the data is not large enough, it can be processed and make prediction from the model. Images are quite similar thus the task became much complicated.

Assignment for the AI for Medicine Specialization course.(1:Build and Evaluate a Linear Risk model/ 2:Risk Models Using Tree-based Models/ 3:Survival Estimates that Varies with Time/ 4:Cox Proportional Hazards and Random Survival Forests)

Prognosis models and apps derived from the Ebola IMC dataset

The field of healthcare is vast, complex, and ever-evolving. Providing accurate and accessible information to patients and professionals alike is crucial for improving healthcare outcomes and fostering a well-informed community. However, sifting through extensive medical literature and resources can take time and effort, even for experts in the fie

This repository contains code for building a random forest model to predict the prognosis of breast cancer patients.

Automatic recommendation of prognosis measures for mechanical components based on massive text mining

Preprocessed data from 2982 de-identified COVID-19 patients, provided by the Albert Einstein hospital, in Sao Paulo, Brazil