Abstract
Worldwide, about 700 million people are estimated to suffer from mental illnesses. In recent years, due to the extensive growth rate in mental disorders, it is essential to better understand the inadequate outcomes from mental health problems. Mental health research is challenging given the perceived limitations of ethical principles such as the protection of autonomy, consent, threat, and damage. In this survey, we aimed to investigate studies where big data approaches were used in mental illness and treatment. Firstly, different types of mental illness, for instance, bipolar disorder, depression, and personality disorders, are discussed. The effects of mental health on user’s behavior such as suicide and drug addiction are highlighted. A description of the methodologies and tools is presented to predict the mental condition of the patient under the supervision of artificial intelligence and machine learning.
Highlights
The term “big data” has become exceedingly popular all over the world.Over the last few years, big data has started to set foot in healthcare system
Healthcare organizations have a big quantity of information available to them and a big portion of it is unstructured and clinically applicable. e use of big data is expected to grow in the medical field and it will continue to pose lucrative opportunities for solutions that can help in saving lives of patients
47% of older adults used the internet versus 87% of younger adults having bipolar disorder Analyzing and storing a large amount of data on MongoDB (i) Developing smartphone application (ii) For mental disorder (iii) For improving etherapies
Summary
Over the last few years, big data has started to set foot in healthcare system. In this context, scientists have been working on improving the public health strategies, medical research, and the care provided to patients by analyzing big datasets related to their health. Big data needs to be interpreted correctly in order to predict future data so that final result can be estimated. To solve this problem, researchers are working on AI algorithms that have a high impact on analysis of huge quantities of raw data and extract useful information from it. A variety of wearable sensors have been developed to deal with both physical and social interactions practically
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