Announcing the latest data release: NeuroBlu NLP, our novel natural language processing approach, has been applied to unstructured clinical notes to enrich the NeuroBlu Database.

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The untapped potential of unstructured notes

In mental health care, most critical clinical insights are captured in unstructured notes or reports. Clinically meaningful data on symptomatology, severity, function, quality of life, and side effect burden exist in free-text fields written by clinicians in the EHR. This information can support piecing together patient journeys, especially when structured measures are not collected at each visit, and the use of standardized assessments and biological tests is inconsistent in behavioral health. However, there have been no scalable ways to analyze these data to draw cohort-level conclusions for clinical research. Traditional approaches such as data abstraction are manual and extremely time and resource intensive.

‍Enrich studies with NeuroBlu NLP-derived structured data

Holmusk has developed novel natural language processing (NLP) models specific to behavioral health to extract key information from unstructured notes. These NLP models are applied to unstructured data and the resulting insights are made available in NeuroBlu, a powerful analytics tool built to explore the world’s richest real-world database for behavioral health. NeuroBlu NLP transforms clinical notes into structured, analyzable, and actionable data not otherwise accessible in EHR or claims data. Applying NeuroBlu NLP to unstructured notes increases data accessibility, data density, and cohort sizes to enhance clinical research for behavioral health.

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Leverage NeuroBlu NLP-derived structured data to:

‍Increase data accessibility

NeuroBlu NLP captures symptomatology data not typically available in structured outcome assessments, providing a more complete picture of a patient’s profile.

Enhance data density

Clinically meaningful information is available for more patient visits over time, enabling a broader and more detailed understanding of individual patient journeys.

Expand cohort sizes

Studies with highly specific inclusion and exclusion criteria become more feasible with access to patients with detailed clinical profiles.

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Explore our NeuroBlu NLP depressive disorders model

NeuroBlu NLP generates analyzable data on symptomatology, such as the presence or absence of anhedonia (decreased interest or pleasure) and suicidality in patients with depressive disorders. Read our latest use case to learn more about the value of NeuroBlu NLP in clinical research and how our depressive disorders model increases cohort sizes by up to 25%. NLP is not only capable of generating queryable data on symptomatology but also on severity, side effects, quality of life, and outcomes. As Holmusk’s NeuroBlu NLP technology advances, models will be applied across additional data partners and developed for new disease indications in behavioral health, such as psychotic disorders.

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Interested in learning more? Contact us to schedule a demo.