John Snow Labs Announces State-of-the-Art Automated Adverse Drug...

John Snow Labs Announces State-of-the-Art Automated Adverse Drug Event Recognition and Classification at the Inaugural NLP Summit

By Healthcare Tech Outlook | Wednesday, October 07, 2020

LEWES, Del. - John Snow Labs, developer of the Spark NLP library, and host of the NLP Summit, today announced the release of its new named entity recognition (NER) model and classifier for Adverse Drug Events (ADE). The announcement takes place on the first day of the company’s inaugural NLP Summit, a free community event focused on applied NLP, being held online from October 6-16.

While the ADE NER model will help extract ADE and drug entities from a given text, the new ADE Classifier is trained on various ADE datasets, including academic texts, social media, and clinical notes. By combining ADE NER and Classifier, John Snow Labs is releasing a new pre-trained clinical pipeline for ADE tasks to save users from building pipelines from scratch. Pretrained pipelines are already fitted using certain annotators and transformers according to various use cases.

According to The Journal of Medical Internet Research, adverse events (AE) in health care entail substantial burdens to health care systems, institutions, and patients, but remain a persistent problem. Negative consequences include extended hospital stays, higher readmission rates, costs, and mortality. Electronic health records (EHR) and natural language processing (NLP) solutions, such as Spark NLP for Healthcare, have made it possible to more effectively and accurately detect prevalent adverse events.

“The enhancements to Spark NLP for Healthcare not only improve the ability to detect adverse drug events, but help prevent the likelihood of future events, thus improving overall care” said David Talby, CTO, John Snow Labs. “These models are pre-trained with Clinical Biobert embeddings, the most powerful contextual language model in the clinical domain today, making it an easy-to-use, best-in-class solution for healthcare NLP projects.”

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