Mapeo conceptual automatizado.
Nuestro sistema automatizado de asignación conceptual es capaz de estandarizar datos al modelo de datos comunes OMOP de forma automática aprovechando las representaciones vectoriales de las descripciones del código fuente interno.
Mapeo conceptual automatizado.
Nuestra tecnología aprovecha el poder de la inteligencia artificial para automatizar la asignación de todos los códigos fuente internos del hospital al modelo de datos comunes OMOP. La automatización de la asignación de códigos se consigue aplicando un enfoque basado en la búsqueda de vectores. Nuestro sistema crea representaciones vectoriales de las descripciones de los códigos internos de los hospitales, que capturan las características sintácticas y semánticas relevantes del texto, garantizando así que registros similares presenten representaciones similares. Por lo tanto, la asignación de códigos estándar se convierte en una tarea de búsqueda de similitudes dentro del espacio vectorial.
Efficient automated mapping of internal source codes to OMOP CDM concepts.
June 3, 2023
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.
Nuestro valor añadido: modelo único
de mapeo conceptual.
Vectorización de las
descripciones de código.
Nuestra tecnología es capaz de realizar un procesamiento previo y posteriormente vectorizar las descripciones internas de los hospitales por medio de un modelo de procesamiento del lenguaje natural.
Validación y
verificación clínica.
Nuestros especialistas clínicos, que son anotadores médicos, validan los resultados del modelo empleando una interfaz interna particular adaptada específicamente a este fin, lo que garantiza la precisión y fiabilidad de los resultados obtenidos mediante nuestra tecnología.
Técnicas de búsqueda
vectorial.
Mediante la aplicación de técnicas de búsqueda vectorial logramos la adquisición de códigos estandarizados, permitiéndonos lograr una perfecta automatización en el proceso de codificación.
Estandarización de los registros en OMOP CDM.
Una vez concluido el proceso de control de calidad, las codificaciones confirmadas se estandarizan al modelo de datos comunes OMOP. Esta integración garantiza la armonización y estandarización de los datos, promoviendo la interoperabilidad y permitiendo que las organizaciones sanitarias lleven a cabo análisis e investigaciones de forma colaborativa.
Certificados por
la EHDEN.
La certificación otorgada por EHDEN acredita nuestra experiencia en el uso del modelo de datos comunes OMOP y representa nuestro compromiso con el fomento de un ecosistema de datos sanitarios estandarizado y eficiente tanto en Europa como en el resto del mundo. Este reconocimiento refuerza nuestra determinación de impulsar la investigación médica, mejorar la atención al paciente y promover la innovación en la asistencia sanitaria a través del uso de datos del mundo real.
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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