ONTOFORCE DISQOVER Natural Language Processing (NLP) (1)

DISQOVER NLP

DISQOVER enhanced with natural language processing (NLP)

Enhance DISQOVER with NLP

DISQOVER’s NLP technology comes with a powerful named entity recognition (NER) and an extraction engine which allows you to quickly find and analyze information about specific biological entities and their relationships (such as diseases, genes, proteins, and more), populations, interventions, and outcomes that are mentioned in large volumes of text data.

All with one single solution.

Key benefits of DISQOVER's NLP functionality

Process public or internal data with pre-configured yet adjustable annotators
One-stop-shop: one single solution for structured AND unstructured data
Modular and flexible: deploy NLP for a single use case or for multiple use cases at once
Expert team of data scientists, machine learning engineers, and bioinformaticians available to support with implementation and user roll-out

Extract more from unstructured data

Uncover insights and knowledge that were previously hidden in text, such as articles, reports, and more. Our NLP annotators have been trained to extract biomedical entities, clinical terms, and relationships from plain text, which can be used to annotate and enrich DISQOVER’s public data and your own private data. On top of this, The NLP functionality can also be utilized on your organization’s archive of consent forms to facilitate clinical data reusability. Further, custom annotators can be developed to screen your information of interest.

Example application: Often, publicly available clinical studies do not specifically annotate patient demographic, intervention, outcome, or biomarker information in a structured way, meaning important knowledge or studies may be missed. Through NLP, DISQOVER can reach this siloed knowledge, add it to the knowledge graph and make it available for search and exploration. 

The NLP functionality is available with the following three models: 

PICOS

model

Retrieves patient population, intervention, comparison, outcomes, and study type data from:

The contents (such as descriptions and inclusion/exclusion criteria) of publicly available publications and clinical studies
Your internal documents, as well as from the data in your own internal repositories such as clinical trial management systems

Biomedical Entities

model

Distills biomedical data from:

The descriptions of clinical trials and literature
Your internal document repositories

Informed Consent

model

Extracts relevant classification for primary and secondary usage of samples and data from consent forms

Key features

Semantic identifiers for the most important concepts in public biomedical documents linked to standardized terminologies
Semantic search capabilities
Concept mapping: harmonized over a variety of vocabularies
Enhanced NLP for proprietary documents