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Medical AI Applications Blog

Case Studies and Lessons Learned from Applying AI in Healthcare and Pharma.

What is Clinical Data Abstraction Creating large-scale structured datasets containing precise clinical information on patient itineraries is a vital tool for medical care providers, healthcare insurance companies, hospitals, medical research,...

Healthcare NLP employs advanced filtering techniques to refine entity recognition by excluding irrelevant entities based on specific criteria like whitelists or regular expressions. This approach is essential for ensuring precision...

In this notebook, RoBertaForQuestionAnswering was used for versatile Named Entity Recognition (NER) without extensive domain-specific training. This blog post walks through the ZeroShotNerModel implementation and explores its ability to adapt...

Leveraging TextMatcherInternal for Precise Phrase Matching in Healthcare Texts The TextMatcherInternal annotator in Healthcare NLP is a powerful tool for exact phrase matching in healthcare text analysis. We’ll cover its...

This blog post compares RxNorm code mapping accuracy and a price analysis between John Snow...