Identifying posttraumatic stress disorder staging from clinical and sociodemographic features: a proof-of-concept study using a machine learning approach

Ramos-Lima, Luís Francisco; Waikamp, Vitória; Oliveira-Watanabe, Thauana Torres; Recamonde‐Mendoza, Mariana; Teche, Stefania Pigatto; Mello, Marcelo Feijó de; Mello, Andréa F.; Freitas, Lúcia Helena Machado

doi:10.1016/j.psychres.2022.114489

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…According to some authors, these methodologies would promote a paradigm shift in the diagnosis, prognosis, monitoring, and treatment of mental illnesses [ 53 ]. One of the most recent research studies in this field is the one performed by Ramos-Lima et al [ 54 ], which investigated the viability of a predictive model to support posttraumatic stress disorders (PTSDs). In that study, a model with four stages suitable for PTSD staging was developed.…”

Section: Discussionmentioning

confidence: 99%

PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia

Martínez-Cao,

Sánchez-Lasheras,

García-Fernández

et al. 2024

Eur. Psychiatr.

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Background. One of the challenges of psychiatry is the staging of patients, especially those with severe mental disorders. Therefore, we aim to develop an empirical staging model for schizophrenia. Methods. Data were obtained from 212 stable outpatients with schizophrenia: demographic, clinical, psychometric (PANSS, CAINS, CDSS, OSQ, CGI-S, PSP, MATRICS), inflammatory peripheral blood markers (C-reactive protein, interleukins-1RA and 6, and platelet/lymphocyte [PLR], neutrophil/lymphocyte [NLR], and monocyte/lymphocyte [MLR] ratios). We used machine learning techniques to develop the model (genetic algorithms, support vector machines) and applied a fitness function to measure the model's accuracy (% agreement between patient classification of our model and the CGI-S). Results. Our model includes 12 variables from 5 dimensions: 1) psychopathology: positive, negative, depressive, general psychopathology symptoms; 2) clinical features: number of hospitalizations; 3) cognition: processing speed, visual learning, social cognition; 4) biomarkers: PLR, NLR, MLR; and 5) functioning: PSP total score. Accuracy was 62% (SD = 5.3), and sensitivity values were appropriate for mild, moderate, and marked severity (from 0.62106 to 0.6728). Discussion. We present a multidimensional, accessible, and easy-to-apply model that goes beyond simply categorizing patients according to CGI-S score. It provides clinicians with a multifaceted patient profile that facilitates the design of personalized intervention plans.

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Section: Discussionmentioning

confidence: 99%

PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia

Martínez-Cao,

Sánchez-Lasheras,

García-Fernández

et al. 2024

Eur. Psychiatr.

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The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis

Wang,

Ouyang,

Jiao

et al. 2024

npj Digit. Med.

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Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.

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Identifying posttraumatic stress disorder staging from clinical and sociodemographic features: a proof-of-concept study using a machine learning approach

Cited by 2 publications

References 33 publications

PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia

PsiOvi Staging Model for Schizophrenia (PsiOvi SMS): A New Internet Tool for Staging Patients with Schizophrenia

The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis

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