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Title: AI & Mental Health
Authors: Kumar, Prasun
Keywords: Mental stress
Financial distresses
Electronic Health Record (EHR)
Electroencephalogram (EEG)
Brain Measurement Data
Magnetoencephalography (MEG)
Functional Magnetic Resonance Imaging (fMRI)
Issue Date: 2021
Publisher: Students of PGDBA Post Graduate Diploma in Business Analytics, IIM Calcutta
Series/Report no.: Vol.2;
Abstract: The COVID-19 pandemic has worsened the situation further. A survey conducted by US Census Bureau revealed that 42% of people reported symptoms of anxiety and depression in December 2020, while it was 11% the previous year (see Figure-1). Similar observations were made from other surveys worldwide. This sudden influx of patients suffering from mental health issues can be attributed to the limited social interactions, fear of illness, financial distresses due to job losses, and others. Amidst all these, one major problem has emerged. Many psychiatrists are reporting burnout due to the increased number of patients and the emotional nature of their work. In such a grim situation where the mental health workforce is in short supply, the advent of AI and Machine Learning has brought extreme hope to solve this task, which has been considered very difficult to tackle. Healthcare is one of the most challenging paradigms for machine learning methods because of the risk associated with the wrong prediction and the lack of suitable data. The human brain has always been a hard nut to crack for scientists. But recently, researchers have made significant progress in applying machine learning techniques to help patients struggling with mental health problems
Appears in Collections:AINA 2.0 - Volume 2 Edition 2020-21

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