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What is SANCARE?

SANCARE is a forward-thinking start-up dedicated to utilizing Machine Learning techniques in the realm of hospital data. We collaborate with top experts to improve our services and offerings. Our platform features a user-friendly and ergonomic design tailored for Medical Information Departments, making it easy for users to adopt and navigate. Users can access a comprehensive range of documents that comprise the electronic patient record, which promotes a seamless experience throughout the process. Our solution acts as an efficient production tool, diligently tracking each step of the coding process for external validation purposes. By harnessing machine learning, we develop robust predictive models that can analyze extensive data sets while taking into account various contextual elements—a capability beyond the reach of traditional rule-based systems and semantic analysis tools. This allows for the automation of complex decision-making processes and the detection of subtle signals that might escape human analysts. The SANCARE machine learning engine operates within a probabilistic framework, enabling it to learn from a vast array of examples to accurately forecast the required codes without direct instructions. In essence, our technology not only simplifies coding tasks but also significantly improves the overall efficacy of healthcare data management. Moreover, by embracing innovative technologies and methodologies, we strive to continually enhance the quality of care provided in the healthcare system.

What is Google Cloud TPU?

Recent advancements in machine learning have ushered in remarkable developments in both commercial sectors and scientific inquiry, notably transforming fields such as cybersecurity and healthcare diagnostics. To enable a wider range of users to partake in these innovations, we created the Tensor Processing Unit (TPU). This specialized machine learning ASIC serves as the foundation for various Google services, including Translate, Photos, Search, Assistant, and Gmail. By utilizing the TPU in conjunction with machine learning, businesses can significantly boost their performance, especially during periods of growth. The Cloud TPU is specifically designed to run cutting-edge AI models and machine learning services effortlessly within the Google Cloud ecosystem. Featuring a customized high-speed network that provides over 100 petaflops of performance in a single pod, the computational power at your disposal can transform your organization or lead to revolutionary research breakthroughs. The process of training machine learning models is akin to compiling code: it demands regular updates, and maximizing efficiency is crucial. As new applications are created, launched, and refined, machine learning models must continually adapt through ongoing training to meet changing requirements and enhance functionalities. In the end, harnessing these next-generation tools can elevate your organization into a leading position in the realm of innovation, opening doors to new opportunities and advancements.

Media

Media

Integrations Supported

Integrations Supported

Cohere
Gmail
Google Assistant
Google Cloud AI Infrastructure
Google Cloud Deep Learning VM Image
Google Cloud Platform
Google Cloud Search
Google Kubernetes Engine (GKE)
Google Photos
Google Translate
Salesforce

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

$0.97 per chip-hour
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Online Training

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

SANCARE

Company Location

France

Company Website

www.sancare.fr/

Company Facts

Organization Name

Google

Company Location

United States

Company Website

cloud.google.com/tpu/

Categories and Features

Machine Learning

Not specified

Categories and Features

AI Infrastructure

Not specified

Machine Learning

Not specified

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