There are 18 repositories under healthcare-datasets topic.
A curated list of awesome open source healthcare tools, algorithms, datasets and research papers.
A curated list of Federated Learning papers/articles and recent advancements.
Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.
Code for WWW2019 paper "A Hierarchical Attention Retrieval Model for Healthcare Question Answering"
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.
Embedded Vision for Baby Behavior Monitoring in IoT
DataSynthesis Platform - Synthetic data building, generating platform for multiple business types
CMSC389I Fall 2018 @ UMD
Synthetic health dataset generator
Data Science tutorials authored by me. Will be updated as I create them/learn them!
This is a repository of links and files with citations of available datasets.
A data parsing script and API providing UK Dictionary of Medicines and Devices (DM+D) data linked to the Global Trade Identification Number (GTIN) of a product.
Deep learning models for classifying ECG time series
Short scripts to demonstrate data available from MolecularMatch API (api key needed). Data includes clinical trials, drugs, publications, molecular information, bioinformatics, report generation and more.
Data sets for the healthyR package.
A simple flask based app for use in healthcare
Physician Office Visit Cost Data :package:
COVID Immunity anonymized testing results registered to the IPFS for public healthcare use.
Medical /Clinical /Healthcare datasets
Importance of HBA1c in predictive Modeling of probability of Hospital Re-admission (CAPSTONE PROJECT)
A self-driven project utilizing ARIMA, Seq2Seq, and XGBoost to help design the COVID19 forecasting algorithm. Data sources are from Kaggle Competition and JHU CSSE.
This repository contains the code and conclusions from a Breast Cancer Detection Machine Learning project. This project using FNA imaging and classification models to determine if breast cancer cells are malignant or benign
Collecting dutch healthcare related opendataset & analyzing important factors for NL coronovirus infected number
This dataset contains the sign and symptom data of newly diabetic or would-be diabetic patients. The data were collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh, and approved by a doctor.
A Spring Boot Application with a Thymeleaf Frontend. Java + Spring Boot framework. MVC and Data transfer Object Design Pattern DTOD), Using an open-source REST client - Spring REST template.
Process chest x-ray image data, varified and labeled by medical professionals. Using TensorFlow and the Keras API, create and validate convolution neural networks that learn to recognize the presence of pneumonia in the lungs.
IoT Healthcare Security Code and Dataset
It is a Capstone project. A model has been created to predict for the heart diseases. It can be very useful for the health sector as cardiovascular diseases are rapidly increasing. The record contains patients' information. It includes over 4,000 records and 15 attributes.
In December 2020, I compiled COVID data from a few different publicly available sources as part of a term project for a class in the Health Informatics and Analytics MSc program at the Tufts University School of Medicine: HIA 217 Multivariable Data Analysis and Visualization. I created a dataset using Python, accessing COVID-19 case and location data through APIs from the US Census and the COVID Tracking Project, and then performed a variety of time-series analysis using linear regression, chi squared tests, and data visualization using Seaborn and Matplotlib in Python and Tableau.
Specific Data Simulators designed to help with the implementation of iDaaS
NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) research creates knowledge about and treatments for the most chronic, costly, and consequential diseases. The dataset used in this project is originally from NIDDK. The objective is to predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Build a model to accurately predict whether the patients in the dataset have diabetes or not.
Apply Data Analytics Techniques on Autism dataset to discover hidden patterns that would be leveraged in decision making.
[Python] Machine Learning models to predict the diabetes status of individuals in the United States