Federated Data Model.
Our commitment is to ensure data security and protection through our Federated Data Model, keeping data always within the hospital facilities.
Federated Data Model.
In an increasingly data-driven and technologically advanced world, the need to safeguard healthcare data privacy and security is paramount.
Our
federated data model
ensures that data never leaves hospital facilities, providing full data protection and compliance with medical privacy regulations. This not only ensures data integrity but also allows hospitals to harness the full potential of their clinical information without compromising patient safety and privacy. In this era of technological advancements and data analytics, our federated model stands as an essential solution that balances innovation with responsibility in healthcare data management.
Our added value: ensuring data protection.
Distributed
SQL.
Distributed SQL empowers massive scalability while retaining
the familiarity of standard SQL.
Multi-
Model.
It seamlessly accommodates relational data models, simplifying the
execution of intricate queries.
Data
Residency.
Manage data storage locations effectively while ensuring data
remains confined within each facility.
High
Resiliency.
Robust and fault-tolerant, featuring inherent network partition
tolerance that grants independence to each facility.
European Healthcare
Data Space.
European Healthcare Data Space has become the cornerstone of digital health in Europe, uniting countries and organizations in the pursuit of more innovative and personalized healthcare ecosystem. At IOMED, we wholeheartedly embrace this initiative and are committed to playing a pivotal role in its realization.
They already
trust us.
"IOMED's commitment aligns with the vision of the European Health Data Space, the definitive initiative of the European Union and associated countries to establish a federated and collaborative framework around clinical data."
"We have experienced a remarkable improvement in our research projects thanks to IOMED. The ability to have our structured and activated data available for various purposes has been crucial."
"We are delighted to have IOMED as our technological ally in the pursuit of innovation in healthcare."
"With IOMED's expertise, we have been able to identify critical touchpoints and gaps in patient journey, allowing us to develop more targeted and patient-centric solutions"
"At IQVIA, we recognize the potential of Natural Language Processing (NLP) technology in transforming healthcare. By partnering with IOMED, we can leverage their advanced data activation technology to improve health outcomes worldwide"
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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