Health

The future of health is here

Probayes can address a wide range of high-added-value use cases on the health market. The health economy is vast, with a very large number of use cases, making it ideal for the implementation of artificial intelligence algorithms. 

The main purpose of these algorithms is to improve diagnostics and patient care.

Probayes develops tools that respond to the health economy’s main challenges:

Algorithms tailored to the health economy

Probayes develops algorithmic solutions to deliver effective decision assistance and make healthcare workers’ day-to-day job tasks easier. We regularly use tools like:

 

Unsupervised classification
To create classes of patients with similar behaviors or symptoms

 

Supervised classification
For assisted diagnostics based on symptoms, test results, age, and other patient information

 

Model explainability
And explainable predictions, a fundamental aspect of the many use cases that require an understanding of the factors that lead to particular behaviors or symptoms

 

Time-series analysis
To make the best possible use of patient medical histories

 

Statistical analysis

To determine statistical significance and the impact of effects observed across several classes of patients

Signal processing

  • To extract useful information from biological signals
  • Healthcare projects require special attention to data anonymization and GDPR compliance
  • Data is stored on secure big data infrastructures that are compliant with GDPR processes and the La Poste company data policy designed to ensure that Probayes services are delivered in accordance with ethical standards. Probayes regularly implements cryptological and anonymization tools

Better results, together

We work hand in hand with your business experts

The health economy is particularly complex. Generally speaking, health projects are characterized by a large number of data sources. At Probayes, we work hand in hand with our customers’ experts to understand the many moving parts of the challenge at hand so that we can develop the best possible solution. We make communication a priority to ensure that we learn from each other throughout the project. Our customers come away with a better understanding of AI implementation in health market scenarios, and our people acquire a deep understanding of the issues our customers face. Each project strengthens our long-lasting customer relationships.

Success stories

Production quality project

Project to improve medical device manufacturing

EU Sensapnea project on sleep apnea

 Project to improve patient data analysis

Eye Tracking project

Project on eye tracking technology for human-machine interaction

Covid X-ray

Open access scripts on model explainability for health applications

Production quality project

Success story

Project to improve medical device manufacturing

Challenge

Entire batches of medical devices can be quarantined or scrapped during manufacturing for non-quality reasons. For manufacturers, understanding why certain specifications essential to product quality are not being met is critical. 

The project

We carried out an initial phase of descriptive data analysis using quality assurance data, obtained primarily from our customer’s in-house software. The data spanned several years and included several product lines.

We then trained artificial intelligence algorithmic models to gain a better understanding of the most important factors affecting final product specifications and how these factors contribute to overall product quality. Our customer’s statistical analysis teams worked with us to guide our approach and enable a deeper understanding of the differences between a “traditional” approach and an AI-based approach.

Results

The results obtained highlighted differences in the analyses of how much different factors affecting the final product specifications contributed to product quality. Our customer was able to improve the quality of products coming off the production line and reduce the number of defective batches.

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