Our technology is capable to unlock variables among millions of data points, facilitating patient recruitment in observational studies by swiftly pinpointing the most relevant factors from vast datasets.
Observational studies are essential in medical research, allowing the analysis of the natural progression of diseases and the effects of treatments on real populations.
We are leaders in Natural Language Processing applied to medical data, and we become a strategic ally to structure and standardize information from a wide range of hospital data sources to find relevant variables. Efficiently and rapidly, our technology identifies variables which meet the specific criteria for the study.
Our technology can automate mapping and structuring data from all hospital data sources, including laboratory results, radiology procedures or pharmacy information. This automated process streamlines the structucturing of diverse data formats into a standardized model.
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.
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.
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.
Collection for CRF.
One of the key outcomes of using IOMED's Natural Language Processing technology in observational studies is a remarkable acceleration in the patient recruitment process. By efficiently and rapidly identifying relevant variables from millions of data points that meet the study criteria, IOMED streamlines the recruitment of eligible patients for the study. This leads to quicker data collection and enables researchers to initiate and complete their studies in a more time-efficient manner.
IOMED's efficient patient recruitment process also results in cost reduction for researchers and medical institutions. By automating the identification of suitable candidates for the observational study, it reduces the need for extensive manual screening and administrative tasks. This cost-saving aspect allows researchers to allocate their resources more effectively, potentially leading to more studies being conducted and contributing to further advancements in medical research.
The extensive representation of patient samples obtained through IOMED's Natural Language Processing systems enhances the validity and relevance of the study's results. By obtaining data from all hospital data sources, researchers can gain strategic insights. This improved data quality helps build a more robust evidence base, fostering advancements in medical knowledge.
Explore other use
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.