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AI’s Role in Detecting Mental Diseases
By Sara Moein  |  Mar 17, 2022
AI’s Role in Detecting Mental Diseases
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Dr Sara Moein discusses the role of artificial intelligence in detecting mental health disease and explains various data structures that AI applies to identify the hidden patterns for mental health diagnosis and treatment.

NEW YORK - Artificial intelligence (AI) methods have recently been introduced to assist mental health providers, including psychiatrists and psychologists, in decision making based on patients’ historical data, such as medical records, behavioral data, social media usage, etc.

In the United States, 43.6 million adults experience mental illnesses such as anxiety, depression, and schizophrenia.1Finding patients with a mental health disease fast is important, since some diseases such as depression may end in suicide. Care providers are, therefore required to be precise and efficient in their decision making.2AI is widely being used to detect this group of disorders and for this purpose methods like machine learning (ML), natural language processing (NLP), and image processing are helpful for automatic diagnosis and treatment.

Multiple Data

Multiple data recourses are available for decision making in the field of mental disease, such as electronic health records (EHR), neuroimaging data, electroencephalogram (EEG), and genetic data. Applying ML methods to each of these resources assists researchers to predict diseases. In some instances, applying ML methods on exome sequence data helps to support researchers in predicting bipolar disease. ML also helps to predict various mental health diseases using gene expression data or detecting genes that have a significant effect in the occurrence of  mental health problems.3, 4

One of the most efficient ways of detecting a mental health disease is with EEG data, that is the recorded activity of the brain from the scalp in the form of a signal. Using ML and intelligent methods, various hidden patterns of the brain are extracted that assist psychiatrists and psychologists in efficient decision making. In order to cope with the limitations of each data set, some r

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