Prediction of quality of life in schizophrenia using machine learning models on data from Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial

Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?

This study evaluated the potential of artificial intelligence (AI) in the diagnosis, treatment, and prognostic assessment of schizophrenia (SZ) and explored collaborative directions for AI applications in future medical innovations. SZ is a severe mental disorder that causes significant suffering and imposes challenges on patients. With the rapid advancement of machine learning and deep learning technologies, AI has demonstrated notable advantages in the early diagnosis of high-risk populations. By integrating multidimensional biomarkers and linguistic behavior data of patients, AI can provide further objective and precise diagnostic criteria. Moreover, it aids in formulating personalized treatment plans, enhancing therapeutic outcomes, and offering new therapeutic strategies for patients with treatment-resistant SZ. Furthermore, AI excels in developing individualized prognostic plans, which enables the rapid identification of disease progression, accurate prediction of disease trajectory, and timely adjustment of treatment strategies, thereby improving prognosis and facilitating recovery. Despite the immense potential of AI in SZ management, its role as an auxiliary tool must be emphasized, with clinical judgment and compassionate care from healthcare professionals remaining crucial. Future research should focus on optimizing human-machine interactions to achieve efficient AI application in SZ management. The in-depth integration of AI technology into clinical practice will advance the field of SZ, ultimately improving the quality of life and treatment outcomes of patients.

Predicting Health-Related Quality of Life Using Social Determinants of Health: A Machine Learning Approach with the All of Us Cohort

This study applied machine learning (ML) algorithms to predict health-related quality of life (HRQOL) using comprehensive social determinants of health (SDOH) features. Data from the All of Us dataset, comprising participants with complete HRQOL and SDOH records, were analyzed. The primary outcome was HRQOL, which encompassed physical and mental health components, while SDOH features included social, educational, economic, environmental, and healthcare access factors. Three ML algorithms, namely logistic regression, XGBoost, and Random Forest, were tested. The models achieved accuracy ranges of 0.73-0.77 for HRQOL, 0.70-0.71 for physical health, and 0.72-0.77 for mental health, with corresponding area under the curve ranges of 0.81-0.84, 0.74-0.76, and 0.83-0.85, respectively. Emotional stability, activity management, spiritual beliefs, and comorbidity were identified as key predictors. These findings underscore the critical role of SDOH in predicting HRQOL and suggests future research to focus on applying such models to diverse patient populations and specific clinical conditions.

From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium and ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence of mental health disorders like depression and schizophrenia, which necessitate precise, innovative approaches. Emerging technologies like artificial intelligence, induced pluripotent stem cells, and multi-omics have the potential to transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies such as animal models and single-variable analyses continue to be used, frequently failing to capture the complexities of human neuropsychiatric conditions. Summary: This review critically evaluates the transition from serendipity to precision-based methodologies in neuropsychiatric research. It focuses on key innovations such as dynamic systems modeling and network-based approaches that use genetic, molecular, and environmental data to identify new therapeutic targets. Furthermore, it emphasizes the importance of interdisciplinary collaboration and human-specific models in overcoming the limitations of traditional approaches. Conclusions: We highlight precision psychiatry's transformative potential for revolutionizing mental health care. This paradigm shift, which combines cutting-edge technologies with systematic frameworks, promises increased diagnostic accuracy, reproducibility, and efficiency, paving the way for tailored treatments and better patient outcomes in neuropsychiatric care.

Autistic trait severity in early schizophrenia: Role in subjective quality of life and social functioning

BACKGROUND

Cognitive impairment is a cardinal feature in patients with schizophrenia and leads to poor social functioning. Recently, the treatment of schizophrenia has evolved to include the goal of improving quality of life (QoL). However, most of the factors influencing subjective QoL are unknown. Autistic traits have been shown to co-occur with various psychiatric conditions including schizophrenia. Hence, the present study aimed to investigate whether cognitive function and autistic trait severity are associated with social functioning and subjective QoL in patients with early schizophrenia.

METHODS

Data were analyzed from 183 outpatients diagnosed with early schizophrenia in Tokyo, Japan. Information was obtained on neurocognition with the Japanese version of the Brief Assessment of Cognition in Schizophrenia. Autistic trait severity was assessed using the Autism Spectrum Quotient (AQ), while social functioning was measured with the Specific Levels of Functioning Scale Japanese version. Information was obtained on subjective QoL with the Subjective Well-being under Neuroleptic drug treatment Short form, Japanese version. Multiple regression analysis was used to examined associations.

RESULTS

In an analysis adjusted for demographic characteristics (age, sex and education), both autistic trait severity (β = -0.56, p < 0.01) and neurocognitive function (β = 4.37, p < 0.01) were significantly associated with social function. On the other hand, only autistic trait severity made a significant contribution to the prediction of subjective QoL (β = -1.79, p < 0.01).

CONCLUSIONS

The results of this study suggest that efforts to detect and treat cognitive impairment and comorbid autistic trait in early schizophrenia may be important for improving social functioning and subjective QoL in this population. In particular intervention that targets autistic trait severity seems to be key to achieving personal recovery in patients with schizophrenia.

Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review

BACKGROUND

An increasing body of literature highlights the integration of machine learning with genomic data in psychiatry, particularly for complex mental health disorders such as schizophrenia. These advanced techniques offer promising potential for uncovering various facets of these disorders. A comprehensive review of the current applications of machine learning in conjunction with genomic data within this context can significantly enhance our understanding of the current state of research and its future directions.

OBJECTIVE

This study aims to conduct a systematic scoping review of the use of machine learning algorithms with genomic data in the field of schizophrenia.

METHODS

To conduct a systematic scoping review, a search was performed in the electronic databases MEDLINE, Web of Science, PsycNet (PsycINFO), and Google Scholar from 2013 to 2024. Studies at the intersection of schizophrenia, genomic data, and machine learning were evaluated.

RESULTS

The literature search identified 2437 eligible articles after removing duplicates. Following abstract screening, 143 full-text articles were assessed, and 121 were subsequently excluded. Therefore, 21 studies were thoroughly assessed. Various machine learning algorithms were used in the identified studies, with support vector machines being the most common. The studies notably used genomic data to predict schizophrenia, identify schizophrenia features, discover drugs, classify schizophrenia amongst other mental health disorders, and predict the quality of life of patients.

CONCLUSIONS

Several high-quality studies were identified. Yet, the application of machine learning with genomic data in the context of schizophrenia remains limited. Future research is essential to further evaluate the portability of these models and to explore their potential clinical applications.

A review on side effect management of second-generation antipsychotics to treat schizophrenia: a drug safety perspective

INTRODUCTION

Effective side effects management present a challenge in antipsychotic treatment with second-generation antipsychotics (SGAs). In recent years, most of the commonly used SGAs, except for clozapine, have been shown to differ only slightly in their effectiveness, but considerably regarding perceived side effects, safety profiles, and compatibility to preexisting medical conditions.

AREAS COVERED

The current state of available evidence on side-effect management in SGA treatment of patients with schizophrenia spectrum disorders (SSD) is reviewed. In addition, current guideline recommendations are summarized, highlighting evidence gaps.

EXPERT OPINION

SGA safety and side effects needs to be considered in treatment planning. Shared decision-making assistants (SDMA) can support patients, practitioners and relatives to orient their decisions toward avoiding side effects relevant to patients' adherence. Alongside general measures like psychosocial and psychotherapeutic care, switching to better tolerated SGAs can be considered a relatively safe strategy. By contrast, novel meta-analytical evidence emphasizes that dose reduction of SGAs can statistically increase the risk of relapse and other unfavorable outcomes. Further, depending on the type and severity of SGA-related side effects, specific treatments can be used to alleviate induced side effects (e.g. add-on metformin to reduce weight-gain). Finally, discontinuation should be reserved for acute emergencies.

Changes in Quality of Life in Treatment-Resistant Schizophrenia Patients Undergoing Avatar Therapy: A Content Analysis

Avatar Therapy has a significant impact on symptoms, beliefs, and quality of life of patients with treatment-resistant schizophrenia. However, little is known about how these changes are implemented into their lives and to which aspects of their lives these improvements relate. Ten consecutive patients enrolled in an ongoing clinical trial were assessed using semi-guided interviews before as well as three months after Avatar Therapy. These encounters have been recorded and transcribed so that the discourse could be thoroughly analyzed, leading to the generation of an extensive theme grid. As the cases were analyzed, the grid was adapted in a back-and-forth manner until data saturation occurred. The content analysis allowed the identification of nine main themes representing different aspects of the patients’ lives, each of which was subdivided into more specific codes. By analyzing the evolution of their frequency, it was observed that, following therapy, patients presented with fewer psychotic symptoms, better self-esteem, more hobbies and projects, and an overall improved lifestyle and mood. Finally, investigating the impact of Avatar Therapy on quality of life allows for a deeper understanding of how people with treatment-resistant schizophrenia can achieve meaningful changes and move towards a certain recovery process.

Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment

PURPOSE OF REVIEW

This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment.

RECENT FINDINGS

Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of the field is the lack of stratification of results based on biological sex, given that psychosis presents differently in men and women; hence, the necessity to tailor identification tools and data analytic strategies. Timely identification and appropriate treatment are key factors in reducing the consequences of psychotic disorders. In recent years, the emergence of new analytical tools based on artificial intelligence such as supervised ML approaches showed promises as a potential breakthrough in this field. However, ML applications in everyday practice are still in its infancy.

Cautionary Observations Concerning the Introduction of Psychophysiological Biomarkers into Neuropsychiatric Practice

The combination of statistical learning technologies with large databases of psychophysiological data has appropriately generated enthusiastic interest in future clinical applicability. It is argued here that this enthusiasm should be tempered with the understanding that significant obstacles must be overcome before the systematic introduction of psychophysiological measures into neuropsychiatric practice becomes possible. The objective of this study is to identify challenges to this effort. The nonspecificity of psychophysiological measures complicates their use in diagnosis. Low test-retest reliability complicates use in longitudinal assessment, and quantitative psychophysiological measures can normalize in response to placebo intervention. Ten cautionary observations are introduced and, in some instances, possible directions for remediation are suggested.