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Publication details how Holmusk's NLP models can transform unstructured psychiatry notes from EHRs into a structured, quantifiable format to enable analysis and rich insights
SINGAPORE and NEW YORK, Feb. 16, 2021 /PRNewswire/ -- Holmusk, a leading global data science and digital health company building the largest Real-World Evidence (RWE) platform for behavioral health, today announced the publication of its scientific article "
Natural Language
Processing-Based Quantification of the Mental State of Psychiatric Patients", in Computational Psychiatry (MIT Press). Full text here: https://cpsyjournal.org/articles/10.1162/cpsy_a_00030/.
With this publication, Holmusk has validated its unique library of proprietary Natural Language Processing (NLP) models that translate unstructured psychiatry notes into quantifiable indicators of patient statuses (e.g., symptoms, side effects, and external stressors). Used to enrich data that reside within health systems, these quantifiable indicators can estimate patient disease severity across the spectrum of behavioral health disorders and create longitudinal trajectories of patient status. By establishing these quantifiable indicators, Holmusk's models generate robust Real-World Evidence of disease progression and treatment efficacies for psychiatric disorders for the first time. Holmusk will use the objective measures from these models to support measurement-based care and personalize care delivery in behavioral health across health systems.