By proactively investing in preventive healthcare measures, life science organizations can accelerate the identification of disease patterns, paving the way for innovative and timely preventive interventions. This proactive approach to healthcare contributes to a brighter and healthier future by reducing the burden of illness, improving overall well-being, and creating a more sustainable and prosperous healthcare ecosystem.
IOMED's approach.
IOMED can help life-science organizations to enhance preventive care in the pharmaceutical and healthcare industry. By structuring and unlocking vast amounts of unstructured clinical data, IOMED´s technology can be leveraged by healthcare organizations to activate valuable data and collaborate with other healthcare professionals .
+21M
Unique Medical Records
+50
Strategic Partners
+30
Years of data
+175
Primary Care Centers
Our technology.
Automated Concept
Mapping.
Our technology can automate mapping and structuring data from all hospital data sources, including laboratory results, radiology reports or pharmacy information. This automated process streamlines the structucturing of diverse data formats into a standardized model.
OMOP
Common Data Model.
Our technology standardizes all hospital data into OMOP Common Data Model. Disposing of standardized data allows healthcare organizations to harness its potential through collaborations with healthcare players and build a data-driven healthcare ecosystem.
Natural Language
Processing.
Our AI-powered technology and Natural Language Processing systems can comprehend free-text inputs from clinical notes and extract relevant information contextually. It's not just about keyword extraction, it's about a deep understanding of the information, ensuring its high-quality through a double quality assurance.
Data Federation
Model.
Our federated data model ensures robust data security by keeping all data within hospital's facilities. This empowers hospitals to engage in research and collaboration while maintaining the utmost data protection, compliant with GDPR.
Significant Outcomes.
Explore other use
cases.
Connecting Healthcare.
We serve as a key ally for healthcare organizations and life-science industry, facilitating the exchange of valuable insights and resources.
Main References.
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.
August, 2023
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.
September, 2023
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.
September, 2023
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.
September, 2021
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, 2022
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.
June, 2023
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.
June, 2020
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