Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach
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 co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Associação Brasileira de Psiquiatria (ABP)
2024-02-01
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| Series: | Brazilian Journal of Psychiatry |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-44462023000600482&tlng=en |
| _version_ | 1797302381102235648 |
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| author | Felipe Dalvi-Garcia Laiana Azevedo Quagliato Donald J. Bearden Antonio Egidio Nardi |
| author_facet | Felipe Dalvi-Garcia Laiana Azevedo Quagliato Donald J. Bearden Antonio Egidio Nardi |
| author_sort | Felipe Dalvi-Garcia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T23:36:00Z |
| format | Article |
| id | doaj.art-f955a657ea86450abe65407e30c6a80c |
| institution | Directory Open Access Journal |
| issn | 1809-452X |
| language | English |
| last_indexed | 2024-03-07T23:36:00Z |
| publishDate | 2024-02-01 |
| publisher | Associação Brasileira de Psiquiatria (ABP) |
| record_format | Article |
| series | Brazilian Journal of Psychiatry |
| spelling | doaj.art-f955a657ea86450abe65407e30c6a80c2024-02-20T07:36:43ZengAssociação Brasileira de Psiquiatria (ABP)Brazilian Journal of Psychiatry1809-452X2024-02-0145648249010.47626/1516-4446-2023-3291Prediction of declarative memory profile in panic disorder patients: a machine learning-based approachFelipe Dalvi-Garciahttps://orcid.org/0000-0003-0544-8034Laiana Azevedo Quagliatohttps://orcid.org/0000-0002-6928-5847Donald J. Beardenhttps://orcid.org/0000-0003-1306-5615Antonio Egidio Nardihttps://orcid.org/0000-0002-2152-4669 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-44462023000600482&tlng=enPanic disordermemorycognitive dysfunctionrandom forest classificationRey auditory verbal learning test |
| spellingShingle | Felipe Dalvi-Garcia Laiana Azevedo Quagliato Donald J. Bearden Antonio Egidio Nardi Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach Brazilian Journal of Psychiatry Panic disorder memory cognitive dysfunction random forest classification Rey auditory verbal learning test |
| title | Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach |
| title_full | Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach |
| title_fullStr | Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach |
| title_full_unstemmed | Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach |
| title_short | Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach |
| title_sort | prediction of declarative memory profile in panic disorder patients a machine learning based approach |
| topic | Panic disorder memory cognitive dysfunction random forest classification Rey auditory verbal learning test |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-44462023000600482&tlng=en |
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