The use of machine learning techniques in trauma-related disorders: A systematic review

Publication date: Available online 6 December 2019Source: Journal of Psychiatric ResearchAuthor(s): Luis Francisco Ramos-Lima, Vitoria Waikamp, Thyago Antonelli Salgado, Ives Calvalcante Passos, Lucia Helena Machado FreitasAbstractEstablishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD) and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice and in academic research, due to clinical and biological heterogeneity. Machine learning (ML) techniques can be applied to improve classification of disorders, to predict outcomes or to determine person-specific treatment selection. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with ASD or PTSD. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to May 2019. We found 806 abstracts and included 49 studies in our review. Most of the included studies used multiple levels of biological data to predict risk factors or to identify early symptoms related to PTSD. Other studies used ML classification techniques to distinguish individuals with ASD or PTSD from other psychiatric disorder or from trauma-exposed and healthy controls. We also found studies that attempted to define outcome profiles using clustering techniques and studies that assessed the relationship among symptoms using network analysis. Finally, we proposed a quality assessment in this review, eval...
Source: Journal of Psychiatric Research - Category: Psychiatry Source Type: research

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