Our Natural Language Processing Systems can structure entities found in human free-text inputs and standardize relevant information contextually.

Contact u​​​​s​​​​​​

Natural Language Processing.

Our Natural Language Processing system represents a revolutionary advancement in the structuration and standardization of clinical data. With the ability to understand free-text inputs from clinical notes, our system can contextualize and comprehend the meaning behind medical information in human-written inputs. It's not just about keyword extraction, it's about a deep understanding of the information. To ensure high-quality, our double quality assurance process allows the attainment of high-quality data to build a data-driven healthcare ecosystem.

A Framework for False Negative Detection in NER/NEL

Finding the false negatives of a NER/NEL system is fundamental to improve it, and is usually done by manual annotation of texts. However, in an environment with a huge volume of unannotated texts (e.g. a hospital) and a low frequency of positives (e.g. a mention of a particular disease in the clinical notes) the task becomes very inefficient.

June 15, 2022

Read more

Our added value: NLP systems and QA processes.


Name Entity 
Recognition (NER).  

Our technology uses advanced techniques such as Name Entity Recognition (NER) to identify and categorize entities like medical conditions, medications, procedures, and measurements embedded within a diverse array of medical texts.

Entity Context 

Our technology understands the specific context surrounding medical entities. This proficiency is instrumental in providing accurate interpretations and a comprehensive understanding of an entity's implications within clinical notes or reports.

Name Entity  
Linking (NEL).

Our cutting-edge technology is capable of seamlessly assigning OMOP (Observational Medical Outcomes Partnership) concept ID's to previous identified entities.

Entity Relation

Our technology recognizes the intricate relationships that interconnect various medical entities within a given text. This analysis unveils the nuanced interplay between medical concepts, contributing to the comprehension of medical narratives.

QA: Identifying 
False Positives.

Our meticulous annotation process, conducted by medical professionals, ensures that any erroneously classified entities are corrected. This approach ensures the precision of our system's outputs and maintains a high standard of accuracy in medical data.  

QA: Identifying False

Our unique, peer-reviewed and published approach involves identifying false negatives by comparing vectorized clinical notes with previously discarded records. This ensures we identify any crucial information missed by the earlier model, enhancing our capacity to detect overlooked relevant data and bolstering the sensitivity of our clinical datasets.

From free - text to standardized data.

By standardizing all hospital data, we ensure consistent and coherent understanding of information across various applications and systems, providing hospitals  with a unique and comprehensive data repository.  


Protected data. 
Empowered healthcare.

We strictly adhere to the principles and regulations set forth by the General Data Protection Regulation (GDPR). Our stringent compliance measures ensure that individual rights and data security are upheld at every step. We are committed to maintain the highest ethical standards while facilitating healthcare players leverage the power of data to drive meaningful healthcare insights.

Explore our 

& QA.

Read​​​​ ​​​​more

Main References.

What's new.

Your Dynamic Snippet will be displayed here... This message is displayed because you did not provided both a filter and a template to use.


I have read and I accept the privacy policy.

Thank You For Your Feedback

Our team will message you back as soon as possible.
In the meantime we invite you to visit our website.

©IOMED 2023

Recinto Modernista Sant Pau
Sant Antoni Maria Claret 167

08025 Barcelona