Shared packages used across Microsoft's health care services
Обновлено 2024-11-22 21:09:28 +03:00
Diagnose AlwaysOn health events like failover events, and failure to failover events
Обновлено 2024-08-22 23:32:19 +03:00
Set of tools for helping with data (in FHIR format) anonymization.
Обновлено 2024-05-31 22:06:46 +03:00
CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.
Обновлено 2023-11-16 20:25:07 +03:00
Sample code and documentation for building virtual meetings with Office 365
Обновлено 2023-07-12 01:57:40 +03:00
Node healthcheck extension for Azure CycleCloud clusters
Обновлено 2023-03-22 01:44:35 +03:00
Machine Learning Patient Risk Analyzer Solution Accelerator is an end-to-end (E2E) healthcare app that leverages ML prediction models (e.g., Diabetes Mellitus (DM) patient 30-day re-admission, breast cancer risk, etc.) to demonstrate how these models can provide key insights for both physicians and patients. Patients can easily access their appointment and care history with infused cognitive services through a conversational interface. In addition to providing new insights for both doctors and patients, the app also provides the Data Scientist/IT Specialist with one-click experiences for registering and deploying a new or existing model to Azure Kubernetes Clusters, and best practices for maintaining these models through Azure MLOps.
Обновлено 2022-12-08 19:18:29 +03:00
Healthcare Blockchain Solution Accelerator
Обновлено 2020-12-18 00:41:48 +03:00
Management Endpoints used to allow insight into your applications
security
metrics
cloud-foundry
steeltoe
health
diagnostics
refresh
thread-dump
tracing-applications
dynamic-logging
environment-variables
heap-dump
information
loggers
Обновлено 2019-05-15 16:55:53 +03:00
Developer samples for Microsoft HealthVault
Обновлено 2018-11-09 20:57:01 +03:00
The HealthVault FHIR Library is a community project started by Microsoft and Get Real Health seeking to produce an open source library for apps that utilize the HL7's FHIR Standard so they can interoperate with the data types used by HealthVault and Get Real Health’s CHBase.
Обновлено 2018-03-15 10:45:11 +03:00
This project contains the HealthVault .NETStandard SDK. This SDK is used to enable cross-platform development using Xamarin, UWP app development, and web development using ASP.NET.
Обновлено 2017-11-18 03:03:52 +03:00
Code for the My Health Clinic web application upgraded to Visual Studio 2017
Обновлено 2017-04-21 20:15:56 +03:00