healthcare-analysis
There are 11 repositories under healthcare-analysis topic.
tuva-health / tuva
Main repo including core data model, data marts, reference data, terminology, and the clinical concept library
nusdbsystem / ARM-Net
A ready-to-use framework of the state-of-the-art models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, anomaly detection, and etc.
Language:Python 67msasnur / Healthcare-Analytics
Hospital admission data was analyzed to accurately predict the patient’s Length of Stay at the time of admit so that the hospitals can optimize resources and function better.
Language:Jupyter Notebook 38gregbeaumont / PowerPopHealth
Power Pop Health is a collection of content intended to simplify the process of ingesting and prepping Healthcare Open Data using Azure data tools and Power BI. Moving forward the overarching theme will be data related to Population Health, but other sources pertinent to Healthcare will also be included.
A starter dbt project and synthetic claims dataset for trying out the Tuva Project.
bukandu1 / Patient-Prime
A web application that assists patients with reviewing objective quality indicators and subjective reviews together along with making an informed decision about their future care.
Language:Python 12- Language:Jupyter Notebook 12
This connector is a dbt project that maps Medicare CCLF claims data to the Tuva Input Layer.
The Tuva Project Docs i.e. where we write and share our knowledge about healthcare data and analytics.
Language:JavaScript 10Maps Medicare LDS claims data to the Tuva Input Layer so you can easily run the Tuva Project.
DiaAzul / dask-modelling-example
Healthcare modelling using Python, Pandas and Dask
Language:Jupyter Notebook 7- heart_disease_analysis
coreycoole / heart_disease_analysis
Statistical analysis of heart disease data project completed during my enrollment in the Data Science program through Thinkful.
Language:Jupyter Notebook 6 - Realtime_HealthCare_AnalyticsLanguage:Python 6
- omopcdm-dot-net
dermatologist / omopcdm-dot-net
:notebook: .Net library for OMOP CDM management
Language:C# 5 Elysian01 / ML-Projects
All of mine ML projects
Language:Jupyter Notebook 5JoannePeel / healthcare-data-analysis
A data and statistics analysis on the institutions in charge of healthcare in Mexico.
Language:Jupyter Notebook 5KhushiBhadange / Exploratory-Data-Analysis-Mental-Health-Problem
In this repository, explore insightful solutions through exploratory data analysis focusing on mental health problems. Gain valuable insights into understanding and addressing key challenges in this critical domain.
Language:Jupyter Notebook 5liyue34673 / Humana_2020_case_competition
Humana-Mays Healthcare Analytics 2020 Case Competition
Language:Python 5spsanderson / healthyR.data
Data sets for the healthyR package.
Language:R 5DNXEMPIRE-1 / Healthcare--Medin
A level 5 Hospital System ------Still Ongoing
Language:HTML 4- Language:Python 3
Kimonokimo / Machine-Learning-Projects
Machine Learning studies at Brandeis University, with my best friends Ran Dou, Tianyi Zhou, Dan Mduduzi, Siyan Lin.
Language:Jupyter Notebook 3NamiF / The-cost-of-healcare-related-to-longevity
Exploratory data analysis to see if there is any country, demonstrating more effective use of Healthcare cost to maintain its longevity than other countries.
Language:Jupyter Notebook 3A dbt project that transforms messy public provider datasets into usable data for the Tuva Project.
- Language:Python 2
JayPat2003 / Brain-Tumor-Detection
Brain Tumor Detection using Deep Learning on AWS SageMaker: A project focused on building and training a deep learning model to detect brain tumors in MRI images. Leveraging AWS SageMaker and Ground Truth, we explore binary classification techniques for accurate diagnosis.
Language:Jupyter Notebook 2- Pneumonia-Recognition-With-Deep-Learning-and-TensorFlow
msikorski93 / Pneumonia-Recognition-With-Deep-Learning-and-TensorFlow
A binary classification using Convolution Neural Network (CNN, or ConvNet) model.
Language:Jupyter Notebook 2 q-maze / sparkHospital
Project to create models to predict ICU patient mortality based on demographic, diagnostic, and other factors utilizing Apache Spark.
Language:Jupyter Notebook 2raj-shr-git / Medicare_Provider_Fraud_Detection
This repository contains the code components of work carried out for analyzing the Medical Provider Fraud Detection dataset with the intent to find most important features to crack down the potentially fraud providers.
Language:HTML 2Taweilo / diabetes-prediction
Leverage Supervised Machine-learning Techiques to Predict Diabetes from Blood Test
Language:Jupyter Notebook 2vinayreddy115 / Healthcare-Quality-and-Expenses-Analysis
Built panel regression model analyzing the variables effecting Healthcare-Expenses and Quality of 26 OECD countries from 2010-16.
Language:R 2ben-a-f / mh-service-model
This repository is a work in progress! The aim is to create a healthcare service capacity-and-demand forecasting tool using discrete-time simulation package SimPy.
Language:Jupyter Notebook 1JaleesMoeen / StrokeScope_EDA
Our analysis delves into a comprehensive examination of stroke data to better understand its risk factors and implications for healthcare. Stroke data analysis serves as a valuable tool for identifying potential risk factors, developing preventive strategies, and enhancing patient care.
Language:Jupyter Notebook 1Lake-Wang / Humana-Healthcare-Case-Competition
Identification of high-risk patients unsuitable for medical procedure. Cleaned claims of inconsistent quality and built meta prediction model. Fairness adjusted AUC score ranked top 20% of more than 200 participating teams.
Language:Jupyter Notebook 1nafisalawalidris / Dr.-Semmelweis-and-the-Discovery-of-Handwashing
Uncover the revolutionary impact of handwashing on mortality rates in healthcare. Explore the story of Dr. Semmelweis and his groundbreaking findings.
Language:Python 1Snigdha1008 / TB_Burden_Analysis
I created various dashboards to ascertain (a)Prevalence of all forms of TB across various countries divided into 6 regions, (b)Distribution of mortality, (c) Evaluation of Mortality (d)Comorbidities with HIV
