Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach
Felipe Dalvi-Garcia1, Laiana Azevedo Quagliato2, Donald J Bearden3
1Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil. Escola de Medicina e Cirurgia, Universidade Federal do Estado do Rio de Janeiro (UNIRIO), Rio de Janeiro, RJ, Brazil.
Revista Brasileira De Psiquiatria (Sao Paulo, Brazil : 1999)
|October 25, 2023
Summary
Random forest models accurately identified declarative memory profiles in panic disorder (PD) patients using clinical data. Body mass index (BMI) emerged as a significant factor influencing memory in PD.
Area of Science:
- Neuroscience
- Psychiatry
- Machine Learning
Background:
- Panic disorder (PD) is associated with cognitive impairments, including declarative memory deficits.
- Understanding these deficits is crucial for developing targeted interventions.
Purpose of the Study:
- To create a classification framework using random forest (RF) modeling.
- To delineate the declarative memory profile in PD patients compared to healthy controls.
Main Methods:
- Developed RF models to classify declarative memory using Rey Auditory Verbal Learning Test (RAVLT) scores.
- Utilized clinical data from 299 PD patients in Rio de Janeiro.
- Included demographic and anthropometric data such as sex, age, and body mass index (BMI).
Main Results:
- RF models achieved high accuracy in predicting declarative memory profiles (AUC > 0.979).
- Models effectively used bias-free clinical data like sex, age, and BMI.
- Body mass index (BMI) was identified as a significant factor in declarative memory.
Conclusions:
- The developed framework accurately classifies declarative memory in PD patients.
- Clinical features, particularly BMI, are important correlates of declarative memory in PD.
- The framework offers a potential tool for future research on PD cognitive function.
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