Prime Your Pharma NLP.
Harness Big Structured & Unstructured Data to Glean Insights & Find Opportunities.
Pharmaceutical companies have an ocean of data at their fingertips – but in a hyper-competitive landscape, the winners are those able to make sense of it all. CapeStart’s in-house team of data preparation experts, medical text datasets and ontologies are a springboard for pharma companies ready for serious NLP-driven innovation.
Harness NLP for Pharma & Win.
Kickstart drug discovery, improve drug repurposing, and identify drug targets and priorities more efficiently through the natural language processing (NLP) of structured and unstructured text, clinical studies, trials, anonymized electronic health records, and even patient forums or social media data.
Our seasoned, in-house team draws on hundreds of text dataset sources and ontologies for the pharmaceutical industry, applicable to a wide range of NLP applications such as medical text classification, named entity recognition, text analysis, and topic modeling.
Make Sense of Big Text Data
Better understand opportunities and possible health risks by quickly analyzing massive amounts of text data from sources like PubMed or ClinicalTrials.gov.
Turn Transcriptions Into Value
Automatically transcribe, process and annotate medical transcriptions such as handwritten prescriptions for NLP applications.
Enhance drug safety and efficacy through NLP on hundreds, to thousands, to even millions of structured or unstructured documents.
Acquire New Targets
Quickly and easily analyze the full text of thousands of patents to find opportunities and realize new drug targets and priorities.
See Us in Action.
Forcasting Drug Approvals.
The client is a full-service life science research and analysis firm that provides drug approval forecasts and drug revenue forecasts based on historical drug approval patterns and competitor revenue trends. Our team performed NER tagging along with annotating FDA proceedings, news articles, and clinical trial results to ensure the speed and accuracy of the client’s ongoing NLP analysis.
Mining for Adverse Drug Reactions.
The pharmacovigilance department of a major pharmaceutical company approached us to build a model that mines for information related to adverse drug reactions from online healthcare forums and social media. Our expert data preparation team sourced, categorized, and labeled millions of social media posts and discussions from online healthcare forums to train the model.
Predicting HCAHPS Ratings.
One of the largest health systems in the U.S. (with more than 150 member hospitals) needed to predict and compare their hospitals’ quarterly HCAHPS ratings, by analyzing their reputation in social media. Our analyst team helped train the client’s NLP models by sourcing, categorizing and sentiment tagging millions of social media posts and online reviews.
Pharma NLP Services.