A Review on Machine Learning Techniques in the Diagnosis of Psychiatric Disorders

Autor:	Rohit, Himanshu Kumar Shukla, Mohit Gangwar, Sapna Singh, Rohit Srivastava, Neha Goyal, Chandrabhan Singh
Rok vydání:	2020
Předmět:	medicine.medical_specialty business.industry Public Health Environmental and Occupational Health Machine learning computer.software_genre Timely diagnosis Identification (information) Neuroimaging Clinical investigation Medicine Artificial intelligence business Psychiatry computer
Zdroj:	Indian Journal of Public Health Research & Development.
ISSN:	0976-5506 0976-0245
DOI:	10.37506/ijphrd.v11i7.10133
Popis:	Diagnosis of psychiatric disorder is intricate clinical entity that could pose challenges for clinicians involving both accurate identification and effective timely diagnosis. These battles have prompted the evolution of multiple machine learning approaches to help improve the management of the disorder. These methods use clinical, anatomical and physiological information and symptoms obtained from neuroimaging and from clinical investigation to make diagnosis system that may identify psychiatric patients as compared to non psychiatric patients and predict diagnosis results. This review paper introduces a background on psychiatric disorder, imaging and machine learning methods. This review paper also discussed about the methodologies of previous studies which have implemented imaging and machine learning in the diagnosis of psychiatric disorder and give directions for future use of machine learning techniques in psychiatric-related studies.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1227badb155e760974af498a4df0df48 https://doi.org/10.37506/ijphrd.v11i7.10133 Zobrazit plný text záznamu