Medicine and Health

Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder

D. Y. Lee, G. Byeon, et al.

This groundbreaking study developed a machine learning model that classifies suicidal thoughts in patients with major depressive disorder using advanced techniques from psychiatry and neuroimaging. By integrating clinical notes and brain MRI data, researchers achieved impressive accuracy in evaluation. The work was conducted by authors Dong Yun Lee, Gihwan Byeon, Narae Kim, Sang Joon Son, Rae Woong Park, and Bumhee Park.

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Abstract

This study developed a machine learning model to classify suicidal thoughts in patients with major depressive disorder using unstructured psychiatric charts and brain MRI. The XGBoost model combined data from clinical notes (topic probabilities from 5 psychiatric symptom topics) and brain T1-weighted MRI (independent components-map weightings from 5 brain networks). The combined model had the highest area under the ROC curve (0.794) compared to clinical notes only (0.748) and brain MRI only (0.738) models. Results were consistent across performance metrics and external validation, suggesting that integrating neuroimaging and NLP variables improves suicidal thought evaluation.

Publisher

Translational Psychiatry

Published On

Jul 04, 2024

Authors

Dong Yun Lee, Gihwan Byeon, Narae Kim, Sang Joon Son, Rae Woong Park, Bumhee Park

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