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huaweicloud_garbage_classify_competiton

identifying-typical-trajectories-in-longitudinal-data

This repository holds code associated with the publication titled: "Identifying typical trajectories in longitudinal data: modelling strategies and interpretations" (https://doi.org/10.1007/s10654-020-00615-6). All code written by Moritz Herle and Bianca De Stavola. This publication is part of a grant led by Nadia Micali funded by the UK Medical Re

informal_latex_template

A informal LaTeX template.

latentclassmultivariatemixedmodels

lcmm

:exclamation: This is a read-only mirror of the CRAN R package repository. lcmm — Extended Mixed Models Using Latent Classes and Latent Processes Report bugs for this package: https://github.com/CecileProust-Lima/lcmm/issues

lcmmohdsi

Extended Mixed Models using latent classes and latent processes dedicated to OMOP-CDM

lctmtools

Latent Class Trajectory Models: An R Package

litrevpy

Package to extract scientific journals from search engines. Automatic download to pdf and save to csv files.

llama-cpp-python

Python bindings for llama.cpp

logistic-regression-from-scratch

I coded logistic regression with gradient descent, using Framingham heart study dataset to predict whether the patient has 10-year risk of future (CHD) coronary heart disease, and do some evaluations based on error rate and McFadden's r2_score

machinelearningformedicalimages

Example code on how to apply machine learning methods to medical images. Contains code (python and python notebooks) and data (DICOM)

medical-image-classification-using-deep-learning

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

medical-image-data

网上获取到一些关于医学图像处理的数据集

medical-image-synthesis

It describes MRI to CT conversion, 3T to 7T conversion using Generative Adversarial Network (GAN).

ml-python

mortality-project

Project to predict mortality from NHANES survey data

mri-to-ct-dcnn-tensorflow

This repository is the implementations of the paper "MR-based Synthetic CT Generation using Deep Convolutional Neural Network Method," Medical Physics 2017.

my-projects

PLAY AROUND DATA!!!

national-health-dataset-dimensionality-reduction-and-clustering

The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. Here, we use the Demographics dataset and reduce its dimensionality by Principal Component Analysis (PCA). Afterwards, we find the main clusters by KMeans Clustering.

network-meta-analysis

HR 网络meta分析代码汇总

nhanes

NHANES data analysis

nhanes-1

NHANES2015-2016

nhanes-2

Machine learning and analysis of heavy metal concentration using the NHANES datasets for 2017-2018.

nhanes-3

Predictive models in Python for Explainable AI

nhanes-4

The main purpose of this project is to develop a model to predict Cardiovascular Disease and its risk factors using NHANES (National Health and Nutrition Examination Survey) dataset.

nhanes-5

Scripts to download and aggregate NHANES data

nhanes-analysis

NHANES data analysis: homework for ALY6140

nhanes-analytics

This repository contains cone for analysis of the NHANES dataset. Specifically, it contains code which will examine the unique food items in the NHANES dietary data. The food items are clustered based on nutrient similarities into new food groups. These food groups represent the result of a data-driven approach of developing food groups for use in dietary analysis studies.

nhanes-blood-health-prediction

nhanes-cancer-prediction