Systematic review of machine learning in PTSD studies for automated diagnosis evaluation

Abstract

Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.

Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights

“…Support Vector Machines, Random Forests, Decision Trees, K-Nearest Neighbors, Gaussian Naïve Bayes, Gradient Boosting, Adaptive Boosting, Random Undersampling Boosting, and Logistic Regression are examples of machine learning algorithms that have proved successful in discovering patterns and predicting outcomes in medical datasets 21,22,23 . Deep learning models, specifically Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have excelled in image identification, natural language processing, and sequential data analysis in the medical field 24,25 .…”