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.

As biopharmaceutical companies and regulatory bodies look toward adopting EHR-derived real-world data to complete studies more efficiently and affordably, one major concern that often arises is what has come to be known as “data missingness.”

Imagine that you are a therapist seeing a new patient for the first time. The patient says that while she has no history of clinical depression, she has been feeling really sad lately and has lost interest in the things she normally enjoys, such as cooking and creating new recipes. 

When you start to ask questions, however, you begin to uncover the factors that may be impacting what your patient is currently experiencing. She tells you she has recently moved across the country and has had trouble finding a new community where she feels connected. Because she feels so sad most of the time, she has stopped calling anyone from her previous home, telling you that she “doesn’t want to bring them down.” You hypothesize that the recent drastic changes in her environment may be contributing to the symptoms of depression she describes.

What if we could better understand the effects of treatments on specific types of individuals, thereby allowing us to personalize drug treatments?

On Clinical Trials Day, we recognize an important part of the healthcare infrastructure: the studies that rigorously evaluate new treatments to determine which will be effective and safe for improving patient health. Through the conventional process of assessment, the treatments that succeed will go on to regulatory approval and will be marketed to patients and their providers.