NLP - MedBioinformatics

NLP: Make your textual data searchable, analyzable and actionable

Imagine the power of finding all the relevant data, in a fraction of the time.

Don’t let valuable insights get lost in your unsearchable, unstructured, free-text documents. Use state-of-the-art NLP solutions to gain deeper insights you wouldn’t otherwise uncover.

By automating information extraction you can streamline your product development process, save time and resources, and make data-driven decisions that optimize your offerings for better business and customer outcomes.

Scientific publications
Electronic health records
Social media content
Industry legacy documents
Clinical trial databases
…and many more!

How it works

1

Unlock the data

Information Extraction Enrichment. Identify and extract relevant data. Key information that was previously unsearchable becomes amenable for downstream analysis.

2

Standardize the data

Semantic Normalization Integration. Ensure consistent data representation and integration through domain-specific ontologies and terminologies, adhering to FAIR principles.

3

Innovate with data

Informed innovation with comprehensive data. Unlock the full potential of comprehensive data exploration and integration. Newly discovered insights empower you to make fully-informed decisions and drive innovation.

The journey of Natural Language Processing in the blink of an eye:

Use case: “Accelerating Drug R&D with NLP”

Pharma · Biotech

Biomedical data is often trapped in free-text resources like publications and clinical records. The unique complexities of biomedical language (terminology, abbreviations, ambiguity) make extracting relevant insights extremely difficult with traditional tools.

Challenge

Scientists often manually review countless research papers and databases to understand diseases and find new drug targets. This task is hampered by the following challenges:

Bottlenecks: The sheer volume of textual data necessitates labor-intensive manual curation, creating bottlenecks and impeding the drug R&D pipeline.

Data Quality: Extracting information from diverse sources with complex terminology and inconsistent formats can introduce inconsistencies and errors, compromising research reliability and reproducibility.

Limited Knowledge Discovery: Human limitations in processing information can lead to overlooked relevant insights, potentially missing crucial drug target candidates.