Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review

Abstract

Background and Aim Schizophrenia and bipolar disorder (BD) are critical and high‐risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease. Methods A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers. Results In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full‐text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity. Conclusion ML can precisely predict results and assist in making clinical decisions‐concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research.

Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: A Scoping Review (Preprint)

“…The application of predictive models to forecast mental health disorders, such as schizophrenia, is gaining importance in medical research [59]. These models hold potential to significantly assist clinicians in patient evaluation, particularly given the heterogeneity inherent to schizophrenia [60].…”

Precision Medicine—Are We There Yet? A Narrative Review of Precision Medicine’s Applicability in Primary Care

“…Artificial intelligence (AI) is increasingly being used to enhance modelling, enabling a broader range of models to be developed at a greater speed [84]. AI-derived models have been shown to be superior, in some but not all examples, to those developed using standard epidemiological methods for predicting disease [85,86], estimating prognosis [87], and predicting all-cause mortality [88]. As discussed previously, the inclusion of PRSs may further enhance the precision of these tools.…”

Clinical Application of Machine Learning Methods in Psychiatric Disorders

“…Deep learning (DL) and neural networks have become increasingly prominent types of ML because of their ability to solve multiple problems, such as natural language processing, speech recognition, and image processing [3] [4]. For example, the said methods can predict negative symptoms of schizophrenia from speech signals and bipolar disorder (BD) from magnetic resonance imaging (MRI) and computed tomography scans [5]. The most preferred algorithms in the medical context include support vector machines (SVMs), random forests (RFs), and gradient boosting (GB) [5].…”

“…For example, the said methods can predict negative symptoms of schizophrenia from speech signals and bipolar disorder (BD) from magnetic resonance imaging (MRI) and computed tomography scans [5]. The most preferred algorithms in the medical context include support vector machines (SVMs), random forests (RFs), and gradient boosting (GB) [5]. Researchers have presented extensive evidence indicating the efficacy of ML models for predicting psychiatric disorders from verbal cues.…”