Medical Records Machine Learning

Using AI to build a scalable & isolated architecture for preprocessing medical records

About
the Client

The challenge

Dealing with complex medical data poses various challenges, including unstructured content with medical terminology and abbreviations, extensive patient histories, and the need for substantial data analysis. Setting up DevOps and configuring environments also demands significant time and effort.

The primary challenge lies in determining the relevance of data due to non-structured information, OCR errors from older EMRs and handwritten notes, diverse medical naming conventions, contradictions, duplications, and the sheer volume of data.

What we did

Rootstrap’s Data Science team manually analyzed medical records and detected different types of problems. This would allow them to create tasks in the machine learning model for each of the problems detected. They used Natural Language Processing (AI for machines to read & understand language) for the extraction of key information to convert clean medical records to a semantic network, following UMLS standards (Unified Medical Language System).

As there is an infinite amount of vocabularies, hierarchies, definitions etc transforming plain text to a semantic network, developing the architecture with this ability to run tasks is the most efficient approach to extract key data.

The Result

Rootstrap’s Machine Learning Engineers withstood this time-consuming and highly complex challenge.

They developed a platform/architecture with a real-time summary of a patient’s clinical encounters for any given date. This AI-driven product can accomplish tasks instantaneously that would otherwise be a time consuming task for humans. They focused their efforts on developing the architecture with the ability to process data in a Unified Medical Language System with over 200 Vocabularies.

By regularly running these types of complex tasks, this machine learning model is constantly improving its capabilities and understanding of medical records terminologies and complex data, and as a result, providing Physicians with instant data visualization in one single location.