AIMC Topic: Schizophrenia
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On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions.
Scientific reports
Dec 30, 2024
Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defined psychopathological picture. Despite many efforts, a definitive disease biomarker has still not been identified. One of the promising candidates fo...
Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.
Schizophrenia research
Dec 27, 2024
Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from...
Immune-based Machine learning Prediction of Diagnosis and Illness State in schizophrenia and bipolar Disorder: How data bias and overfitting were avoided.
Brain, behavior, and immunity
Dec 17, 2024
In a letter critiquing our manuscript, Takefuji highlights general pitfalls in machine learning, without directly engaging with our study. The comments provide generic advice rather than a specific critique of our methods or findings. Despite raising...
Predictive utility of artificial intelligence on schizophrenia treatment outcomes: A systematic review and meta-analysis.
Neuroscience and biobehavioral reviews
Dec 4, 2024
Identifying optimal treatment approaches for schizophrenia is challenging due to varying symptomatology and treatment responses. Artificial intelligence (AI) shows promise in predicting outcomes, prompting this systematic review and meta-analysis to ...
AI-based medication adherence prediction in patients with schizophrenia and attenuated psychotic disorders.
Schizophrenia research
Dec 4, 2024
OBJECTIVE: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event.
Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques.
Sensors (Basel, Switzerland)
Dec 4, 2024
Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characteriz...
RNA Editing Signatures Powered by Artificial Intelligence: A New Frontier in Differentiating Schizophrenia, Bipolar, and Schizoaffective Disorders.
International journal of molecular sciences
Dec 3, 2024
Mental health disorders are devastating illnesses, often misdiagnosed due to overlapping clinical symptoms. Among these conditions, bipolar disorder, schizophrenia, and schizoaffective disorder are particularly difficult to distinguish, as they share...
Reevaluating feature importances in machine learning models for schizophrenia and bipolar disorder: The need for true associations.
Brain, behavior, and immunity
Nov 29, 2024
Skorobogatov et al. developed supervised machine learning models to predict diagnoses and illness states in schizophrenia and bipolar disorder. However, their reliance on bootstrap forests and generalized regressions introduces significant biases in ...
Facial expression analysis using convolutional neural network for drug-naive and chronic schizophrenia.
Journal of psychiatric research
Nov 29, 2024
OBJECTIVE: Facial images have been shown to convey mental conditions as clinical symptoms. This study aimed to use facial images to detect patients with drug-naive schizophrenia (DN-SCZ) or chronic schizophrenia (C-SCZ) from healthy controls (HCs), a...
Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.
Journal of medical Internet research
Nov 13, 2024
BACKGROUND: Assessing the complex and multifaceted symptoms of patients with acute psychiatric disorders proves to be significantly challenging for clinicians. Moreover, the staff in acute psychiatric wards face high work intensity and risk of burnou...
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