Our expert Linguamatics professional services team can deliver on all your text analytics and knowledge discovery needs, providing essential NLP-based data insights to speed drug development and improve patient outcomes.
For over 15 years, leading pharma, healthcare and government organizations have trusted Linguamatics to deliver actionable insights that address their most pressing bench-tobedside challenges, with quantifiable ROI across multiple real world use cases. By breaking down data silos, boosting innovation, enhancing quality, and reducing risk and complexity, we empower our clients to speed up drug development and improve patient outcomes.
The Linguamatics professional services team brings a unique blend of in-depth industry expertise in life science, healthcare, text mining and natural language processing (NLP) to help our customers solve their most challenging information extraction and knowledge discovery issues. Our team has experience in projects and solutions across the drug discovery and development cycle, real world data, electronic health records and more. We can work with multiple formats and types of data, including internal documents, scientific literature, patent documents, call centre feeds, pathology reports, nurses’ notes and radiology reports.
Over the years, we have worked on hundreds of customer projects across both pharma and healthcare organisations. For example:
Mundipharma Research Limited implemented a project using Linguamatics NLP solution, to find, highlight and extract data elements for Iteration 1 from unstructured documents such as the EMA Summary of Product Characteristics (SmPC) documents.
Our experts developed algorithms to extract the individual data elements using standard and customized ontologies, as well as linguistic features and region structures of SmPCs. Accuracy was evaluated against a ‘gold standard’ data set that had been manually extracted by an independent expert.
“We were really impressed when we saw the accuracy with which Linguamatics NLP had been able to extract data elements from the documents”.
Jon Sanford, Head of Regulatory Information Management and Operations at Mundipharma Research
Pfizer Automated Quality Review
Checking for errors in documents for FDA submission is a complex problem, which is currently undertaken as a manual and costly process. Automation of this process could speed it up and make it more efficient.
Pfizer and Linguamatics have worked together on a project to create a solution that allows reviewers to submit document packets and use Linguamatics NLP to generate reports summarizing the detected errors. The solution was implemented on premise at Pfizer, ensuring that the sensitivity of these documents is preserved.
Merck SALAR KAT (Knowledge Access Tool)
Linguamatics Professional Services worked on a project with the Safety Assessment and Laboratory Animals Resources (SALAR) division at Merck MSD. This division helps advance high quality drug candidates into development by defining the non-clinical safety and selectivity of lead compounds.
Together, we developed an automated workflow to extract unstructured conclusions and interpretations from final study reports, ante-mortem reports, post-mortem reports and protocols stored in a Documentum-based electronic official file repository. The Linguamatics NLP algorithms developed were able to identify, extract, and normalize study annotation metadata and organ pathology findings. The results are combined with structured output, loaded into a SALAR knowledgebase, and visualized via dashboards for the safety assessment teams.