– Case Study –
Text Annotation for Medical Research Articles
Leading biomedical platform boosts customer satisfaction with automated curation and indexing of articles on drugs, diseases, and medical device data
A leading biomedical and pharmacological literature database provides research institutions and hospitals with the most up-to-date articles and studies so that they can make the best-informed medical decisions for their patients. To keep their database up to date the client wanted a way to automate the ongoing curation and indexing of articles and data while ensuring that the integrity and relevancy of the articles included is maintained.
Automated Curation & Indexing
The Innodata team had to annotate highly technical and contextual research articles related to drugs, diseases, and medical devices. In order to annotate hundreds of thousands of medical and pharmaceutical research articles, Innodata deployed a machine learning based production platform that found, ingested, and did first pass auto annotations of the articles. To achieve high quality annotations and ensure the right context and most relevant information was annotated, the annotation team was trained and overseen by subject matter experts, such as physicians and pharmacists. Annotators then did an additional pass over the articles to make necessary changes from the initial ML annotated pass.
With the delivery of the high-quality annotated articles, the biomedical and pharmacological data company was able to expand the functionality of their platform. For example, it was able to now link relevant diseases, drugs, and medical devices. The overall accuracy of the customer’s platform and database was improved and is kept current, so that they are able to maintain a competitive edge and better satisfy their customers.
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