Medical records entity detection

We assisted AEMPS in transforming the landscape of medical research by developing an advanced AI model for medical information recognition. This breakthrough technology is designed to accurately identify and process medical data, significantly enhancing research efficiency and accuracy in the medical field.


AEMPS, a key player in the medical research field, holds an extensive collection of more than 20 million anonymized medical records, intended for advancing research efforts. These records represent a rich source of information, but their potential remains untapped due to their unstructured format. The lack of structure in this vast amount of data presents a significant barrier, as it makes the extraction of meaningful insights for research purposes challenging. Overcoming this obstacle is crucial for AEMPS to fully leverage this valuable resource and contribute effectively to medical research and innovation.


The primary objective of our project with AEMPS is to generate a robust training dataset, essential for developing a sophisticated Named-Entity Recognition (NER) machine learning model. This model will be specifically trained to identify and extract key medical entities such as clinical findings, medications, and dosages from the extensive unstructured data. The ultimate goal is to empower researchers by providing them with precise and actionable insights, enhancing the efficacy of medical research and facilitating advancements in the healthcare sector.


As a result of our collaboration with AEMPS, we successfully developed a state-of-the-art machine learning model that revolutionizes the way researchers access and analyze information. This model, designed with advanced Named-Entity Recognition capabilities, now serves as a powerful tool for efficiently sifting through the vast database of 20 million medical records. It enables researchers to rapidly locate and extract critical information such as clinical findings, medications, and dosages. This breakthrough significantly streamlines the research process, unlocking new potentials in medical research and facilitating quicker, more informed decision-making in the healthcare industry.