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Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, develo** drugs for central nervous system (CNS) disorders remains the most …
However, develo** drugs for central nervous system (CNS) disorders remains the most …
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future …
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …
[HTML][HTML] Artificial intelligence for brain diseases: A systematic review
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for
analyzing complex medical data and extracting meaningful relationships in datasets, for …
analyzing complex medical data and extracting meaningful relationships in datasets, for …
[HTML][HTML] One size does not fit all: methodological considerations for brain-based predictive modeling in psychiatry
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way
across individuals, and no two patients with a shared diagnosis exhibit identical symptom …
across individuals, and no two patients with a shared diagnosis exhibit identical symptom …
Challenges and future prospects of precision medicine in psychiatry
Precision medicine is increasingly recognized as a promising approach to improve disease
treatment, taking into consideration the individual clinical and biological characteristics …
treatment, taking into consideration the individual clinical and biological characteristics …
A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits
TD Habtewold, LH Rodijk, EJ Liemburg… - Translational …, 2020 - nature.com
To tackle the phenotypic heterogeneity of schizophrenia, data-driven methods are often
applied to identify subtypes of its symptoms and cognitive deficits. However, a systematic …
applied to identify subtypes of its symptoms and cognitive deficits. However, a systematic …
Towards artificial intelligence in mental health: a comprehensive survey on the detection of schizophrenia
Abstract Computer Aided Diagnosis systems assist radiologists and doctors in the early
diagnosis of mental disorders such as Alzheimer's, bipolar disorder, depression, autism …
diagnosis of mental disorders such as Alzheimer's, bipolar disorder, depression, autism …
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
By promising more accurate diagnostics and individual treatment recommendations, deep
neural networks and in particular convolutional neural networks have advanced to a …
neural networks and in particular convolutional neural networks have advanced to a …
Map** Brain Synergy Dysfunction in Schizophrenia: Understanding Individual Differences and Underlying Molecular Mechanisms
C Ding, A Li, S **e, X Tian, K Li, L Fan, H Yan… - Advanced …, 2024 - Wiley Online Library
To elucidate the brain‐wide information interactions that vary and contribute to individual
differences in schizophrenia (SCZ), an information‐resolved method is employed to …
differences in schizophrenia (SCZ), an information‐resolved method is employed to …
Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review
M Montazeri, M Montazeri… - Health science …, 2023 - Wiley Online Library
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 …
risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population …