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.