The Hol Picture

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.

Real-world evidence has been increasingly recognized as an important complement to clinical research. In clinical research, randomized controlled trials often have stringent inclusion criteria which may exclude certain patient groups, such as those with comorbidities. When clinical trial populations diverge from patient populations seen in a real-world care setting, insights gained might not be widely applicable to all people. Analyzing real-world data, such as clinical information recorded in electronic health records (EHR) as part of routine clinical care, can provide insights that are generalizable to real-world populations.

Consider an emergency room doctor who is caring for a patient who has been hospitalized after a suicide attempt. When she consults his chart, she can see that he has been diagnosed with depression, but the structured data that is immediately available does not provide much additional context. 

This blog post is a continuation of our Data Differentiators series, in which we’re sharing how the NeuroBlu Database stands out from other EHR-derived real-world data sources. For previous installments of the series, see here and here.