Patient Characterization: our Data Solution for Observational Studies

AI-powered technology for Healthcare Data Activation.

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Observational studies are essential in medical research, allowing the analysis of the natural progression of diseases and the effects of treatments on real populations.

Patient Characterization

Patient Characterization Data Solution offers a streamlined approach, combining advanced technology, clinical expertise, and rigorous statistical methods for high-quality data in retrospective observational studies.

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.

Comprehensive Data Catalog
+15 Federated Hospitals
Custom Data Cohorts
All EHR data points including free-text
OMOP CDM Format Deliverable
Added value
Data depth
Information understanding
Our technology understands temporal nuances, negation, and complexities in clinical data, providing unique contextual depth for detailed and meaningful analyses.
Data Quality & Accuracy
Expert Annotators & Clinical Validation
We verify data by physicians and statistical tests, to validate the consistency and coherence of extracted data, guaranteeing data meets high standards, reliability and precision.
Faster Data availability
Federated Data Netowork
Significant time saving by leveraging our extensive data federated network,composed of hospitals which already have all its data structured and normalized.
Analytical efficiency
Standarized OHDSI tools
IOMED adopts the European standard OMOP CDM, to enhance data interoperability, and facilitating to leverage OHDSI's extensive tools for enhanced analytical efficiency.
Significant outcomes
Accelerated Data Collection for CRF
Cost reduction
Improved Data Quality
Informed Decision-Making
Real-world data Insights
Main references
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.

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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.

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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.

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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.

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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.

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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.

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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.

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