Project AI4EU-CODE (Classify Oncology Diseases: Español) is an EU project that addresses the challenge of inferring meaningful insights out of electronic health records (EHRs) and aims to enable healthcare providers to improve the quality of their services, especially in oncology. The project is funded by the European Union’s Horizon 2020 research and innovation programme as an AI4EU Solution. It began on 23.06.2021 and will end on 16.12.2021.
AI4EU-CODE’s main objective is to develop a Natural Language Processing tool that will automatically predict ICD-10 (International Classification of Diseases, Tenth Revision) codes from free-text. The project will apply state of the art deep learning models, which will be adapted and fine-tuned for the specific task and type of textual content.
An important aspect of the project is to provide multilingual support, ensuring the capability to classify Spanish EHR medical notes. AI4EU-CODE’s ambition is to propose a more general multilingual solution that can be easily fine-tuned for other European languages.
Ontotext has joined AI4EU-CODE for 6 months as the solution provider for this challenge. This new diagnosis text-based classification method will complement Ontotext’s current commercial offering for Medical Coding of unstructured content. By taking part in this project, Ontotext wants to contribute to the introduction of new, innovative workflows that will shorten the time and will lower the cost of medical service delivery.
The medical coding market was valued at $15,259.67 million in 2020, and it is expected to reach USD 28,363.63 million by 2026, registering a CAGR of 10.93%, during the forecast period (as of July 2121). The majority of the current service providers rely on manual coding workflows where quality is ensured by domain experts. Regrettably, the percentage of medical coding services based on the automated medical information extraction and classification is still very low.
The ability to automatically annotate clinical texts with ICD-10 codes and validate clinical descriptions against diagnoses by medical professionals is highly desired by healthcare institutions as well as scientific organizations. It will reduce the time for describing the documentation and will assist decision-making in diagnosis, which will result in increased patient treatment time.
Learn more about the CODE project!
For more information, contact Doug Kimball, Chief Marketing Officer at Ontotext