Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach
Braz. J. Psychiatry (São Paulo, 1999, Impr.)
;
45(6): 482-490, Nov.-Dec. 2023. tab, graf
Artículo
en Inglés
|
LILACS-Express
| LILACS
| ID: biblio-1533996
ABSTRACT
Objective:
To develop a classification framework based on random forest (RF) modeling to outline the declarative memory profile of patients with panic disorder (PD) compared to a healthy control sample.Methods:
We developed RF models to classify the declarative memory profile of PD patients in comparison to a healthy control sample using the Rey Auditory Verbal Learning Test (RAVLT). For this study, a total of 299 patients with PD living in the city of Rio de Janeiro (70.9% females, age 39.9 ± 7.3 years old) were recruited through clinician referrals or self/family referrals.Results:
Our RF models successfully predicted declarative memory profiles in patients with PD based on RAVLT scores (lowest area under the curve [AUC] of 0.979, for classification; highest root mean squared percentage [RMSPE] of 17.2%, for regression) using relatively bias-free clinical data, such as sex, age, and body mass index (BMI).Conclusions:
Our findings also suggested that BMI, used as a proxy for diet and exercises habits, plays an important role in declarative memory. Our framework can be extended and used as a prospective tool to classify and examine associations between clinical features and declarative memory in PD patients.
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Índice:
LILACS (Américas)
Idioma:
Inglés
Revista:
Braz. J. Psychiatry (São Paulo, 1999, Impr.)
Asunto de la revista:
Psiquiatria
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
Brasil
/
Estados Unidos
Institución/País de afiliación:
Childrens Healthcare of Atlanta/US
/
Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro (UFRJ)/BR
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