Research and Google Trend for Human Neuropsychiatric Disorders and Machine Learning: A Brief Report.

OBJECTIVES

The key characteristics of this study are to highlight the research trend pertaining to the use of machine learning in the diagnosis and management of neuropsychiatric conditions.

METHODS

The last ten years (2011-2020) Scopus data related to the use of machine learning techniques in the diagnosis and management of neuropsychiatric disorders in human beings have been collected and examined using VOSviewer. The global internet trend for neuropsychiatric disorders and machine learning techniques during the observation period (1-Jan-2010 to 30-Nov-2020) has been also explored using Google Trend.

RESULTS

The mean values of the Google trend for neuropsychiatric disorders and machine learning are 52.09 and 40.00 respectively. Moreover, the correlation coefficient for the Google trend of USA, UK and the world found to be significantly (0.98) higher. Likewise, the mean values of web trend for USA, UK, and China are 42.17, 38.55, and 30.90. Additionally, the Google trend for the term 'machine learning' in the observation period (1-Jan-2010 to 30-Nov-2020) has been also explored.

CONCLUSION

It is observed that the researchers from the US (32.4%), UK (9.2%) and China (7.4%) are the prime contributors as far as mining and management of the neuropsychiatric disorders using machine learning is concerned. Moreover, the study revealed that neuropsychiatric disorders (seizure, eating, mood, sleep, conduct, and intellectual) need more attention as far as machine learning is concerned.