The client

DXA is the result of more than 2 years of research and development by experts in the field of medical technologies. By reviewing more than 16 000 articles, the team at DXA incorporated the most up to date data in the solution which is based on an intelligent algorithm. The tool redefines the actual diagnosis process. 

This solution gathers relevant medical information on a patient by using an automated and intelligent questionnaire that focuses on the patient's grounds for consulting in a primary care context. The process is very simple and quick and can be completed in the patient's mothertongue. This provides a reliable and safe information collection. Afterwards, the tool populates a detailed medical history that will then be prioritized according to the Canadian scale of triage as well as the severity provided by incorporating the vital signs and others symptoms whithin the tool. 

The situation

The intelligent algorithm's biggest efficiency is mostly based on the quick establishment of the patient's initial reason for consulting. Therefore, the team at DXA tried thinking about an intelligent classification that would demonstrate the most common and least common reasons of consultation. In a context of primary care and with various reasons of consultation, they discovered that it was hard to find a solution with potential.

Also, the accumulation of data and its aggregation by the intelligent questionnaire as well as the comparison with the final diagnostic were thought about in order to later refine the tool by using machine learning. Therefore, the team at DXA called upon ML+'s expertise to help them in these areas.  

The solution

The Ml+ team is in charge of structuring the data gathered in order to integrate machine learning which will be beneficial for the tool. Language processing algorithms were also developped in order to quickly determine the reasons of consultation. By developping a labeled text corpus, the algorithms can associate the words with a subject or a precise category of reason of consultation. By integrating the automatic language processing, it simplified the use of the tool for everyone. It also increased the classification's precision while reducing the time necessary to answer the questionnaire. 

Appreciation

The ML+ team was always able to deliver quality work while respecting the deadlines set. The medical field is a complicated field, but that was never a problem for ML+. The work done exceeded the expectations of the direction team at DXA and added an enormous value to the product. The team at DXA plans on pursuing its work with ML+ and wouldn't consider any other team for its artificial intelligence and machine learning needs. 

 

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