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Mental health in China is a growing issue. Experts have estimated that about 130 million adults living in China are suffering from a mental disorder. [1] [2] The desire to seek treatment is largely hindered by China's strict social norms (and subsequent stigmas), as well as religious and cultural beliefs regarding personal reputation and social harmony.
Additionally, the prevalence of mental health and addiction disorders exhibits a nearly equal distribution across genders, emphasizing the widespread nature of the issue. [9] The use of AI in mental health aims to support responsive and sustainable interventions against the global challenge posed by mental health disorders.
In 2001, the CSP declassified homosexuality and bisexuality as a mental disorder. [5] [6] [7] However, the organization specified that, "although homosexuality was not a disease, a person could be conflicted or suffering from mental illness because of their sexuality, and that condition could be treated", according to Damien Lu, founder of the Information Clearing House for Chinese Gays and ...
Bayesian learning mechanisms are probabilistic causal models [1] used in computer science to research the fundamental underpinnings of machine learning, and in cognitive neuroscience, to model conceptual development. [2] [3]
The China Brain Project is a 15-year project, approved by the Chinese National People's Congress in March 2016 as part of the 13th Five-Year Plan (2016–2020); it is one of four pilot programs of the Innovation of Science and Technology Forward 2030 program, targeted at research into the neural basis of cognitive function.
Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.
Bayesian cognitive science, also known as computational cognitive science, is an approach to cognitive science concerned with the rational analysis [1] of cognition through the use of Bayesian inference and cognitive modeling. The term "computational" refers to the computational level of analysis as put forth by David Marr. [2]
Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. It is based on Bayesian inference to interpret the observations/data acquired during the experiment. This allows accounting for both any prior knowledge on the parameters to be determined as well as ...