The Hol Picture

Our Insights on Real-World Evidence and Behavioral Health

Posts by:

Deepali Kulkarni

Deepali is a seasoned data scientist with a PhD and over 7+ years of experience in the dynamic field of Data Science. Her journey extends beyond industry, having been a researcher in academia for over 7 years. She is committed to blending research and practical applications, especially in the realm of mental health. With over 4+ years of expertise in Natural Language Processing, Deepali’s focus extends to unraveling the intricacies of language, crafting algorithms, and exploring the vast possibilities at the intersection of technology and mental health.

A team of colleagues working on oversized gears

NeuroBlu NLP: A Technical Deep Dive into Developing NLP models for Clinical Insights

The landscape of mental health research is evolving, and tools like Natural Language Processing (NLP) hold immense potential to bridge the gap between clinical trials and real-world evidence. As we strive to enhance our understanding of mental health outcomes and treatment effectiveness, a strategic implementation of NLP tools becomes pivotal.

This blog explores a high-level overview of how we developed NeuroBlu NLP - Holmusk’s NLP models specifically tailored for extracting disease-specific clinical features from unstructured clinical text. These NLP-derived clinical features are then integrated into our structured data and made available in NeuroBlu, Holmusk’s powerful data analytics software for behavioral health. The incorporation of NLP-derived features into structured data provides a more comprehensive, easily accessible estimate of patient phenotypes in the real-world, ultimately benefiting patient care.

Read More