Leveraging healthcare data.
We are the key ally for healthcare organizations, revolutionizing the way they gain insights.
This is what we do
Shaping the future of healthcare.
IOMED leverages the power of Artificial Intelligence and Natural Language Processing to unlock structured and unstructured healthcare data. Our cutting-edge technology break down the barriers of language and software systems, by efficiently mapping and structuring data from any source within the hospital's systems, and standardizing it into OMOP Common Data Model, guaranteeing data quality and consistency, and strictly compliant with GDPR and bioethical considerations. This allow healthcare organizations to gain insights and build a data-driven healthcare ecosystem.
Our added value.
Our key differentiator lies in the use of
AI-based technology and Natural Language Processing systems
, which through a unique methodology and quality assurance processes, unlocks both structured and unstructured healthcare data.
Federated Data Model
allows hospitals to store its own data within the hospital facilities, guaranteeing full protection and availability of high-quality structured healthcare data.
Our technology is capable of standardizing all clinical data from hospital's information systems into the OMOP Common Data Model, allowing healthcare organizations to harness its potential.
We lead the healthcare transformation through data unlocking, breaking down previously existing barriers and
unleashing the potential of Real-World Data
. Our technology allows healthcare players to collaborate to make informed decisions and improve patient outcomes.
We empower healthcare
Our technology enables medical professionals to understand a wealth of information, analyze trends, and make data-driven decisions to enhance patient care and hospital outcomes. By leveraging the power of Artificial Intelligence, healthcare organizations can extract actionable data and become part of the healthcare ecosystem of the future.
The DERMACLEAR study: Verification results of a natural language processing system in dermatology.
Results from the DERMACLEAR study will increase the real-world evidence of clinical practice, obtaining a large amount of information on patients with the studied diseases. The NLP system used is precise in identifying patients diagnosed with HS, PsO, CU and/or AD, and other medical variables from EHRs, highlighting that it is a valid system to use in the DERMACLEAR study.
An open source corpus and automatic tool for section identification in Spanish health records.
This work shows that it is possible to build competitive automatic systems when both data and the right evaluation metrics are available. The annotated data, the implemented evaluation scripts, and the section identification Language Model are open-sourced hoping that this contribution will foster the building of more and better systems.
NATI (NATural language in ThyroId cancer).
A total of 5137 medical records of patients diagnosed with thyroid cancer between 2015 and 2022 were included. The median follow-up (interquartile range) was 29.7 months (8.8-55.8). The mean age at the time of diagnosis was 55 years (SD 18), and 67% were women. The stage could be classified in a subgroup of 520 patients, of which 60% (n=313) had advanced stages. Metastasis was observed in 2177 patients (42%) during the follow-up, mainly in lymph nodes (44%). It was also identified that the majority of patients (71%; n=3629) had some comorbidity.
Extending the OMOP CDM to store the output of natural language processing pipelines.
Although OMOP CDM provides a NOTE_NLP table to store the outputs of NLP algorithms, queries to this table can become clumsy and slow, so we designed and extended the OMOP CDM with our own NLP schema to store the results generated in the annotation process of NLP. We designed an extension of the OMOP CDM able to store the output of NLP solutions while integrating with the vocabulary normalization process of the OMOP CDM.
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.
Efficient automated mapping of internal source codes to OMOP CDM concepts.
Our automated concept mapping system provides an efficient way of mapping source codes to OMOP concepts. By utilizing text-based vector representations and knowledge transfer, our system can find equivalent mappings from other hospitals, thereby reducing the time and effort required for manual mapping.
ContextMEL: Classifying Contextual Modifiers in Clinical Text.
Taking advantage of electronic health records in clinical research requires the development of natural language processing tools to extract data from unstructured text in dif ferent languages. A key task is the detection of contextual modifiers, such as understanding whether a concept is negated or if it belongs to the past. We present ContextMEL, a method to build classifiers for contextual modifiers that is independent of the specific task and the language, allowing for a fast model development cycle.