Home / Current Issue / Paper 1703849
AI in Mental Health: Predictive Analytics for Early Detection and Intervention in Psychiatric Disorders
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Artificial intelligence (AI) technology proves useful to mental healthcare through predictive analytics, which identifies early-stage psychiatric disorders. AI uses machine learning algorithms to examine large volumes of clinical data that reveal recognizable indicators of depression, anxiety, and schizophrenia before professional diagnosis. The timely identification through early detection enables medical professionals to start proper treatments, increasing favorable treatment results and lowering long-term medical expenses. Numerous analytical techniques, such as data mining, natural processing, and deep learning methods, are used to model mental health disorders and subsequent risk potentials. Incorporating artificial intelligence into psychiatric care systems provides patients with custom-made therapeutic solutions and continuous health checks. This article examines existing AI implementations in psychiatry to demonstrate their effects while discussing obstacles alongside expected developments that will improve healthcare delivery for patients and mental health service operations.
Keywords
Artificial Intelligence, Machine Learning, Predictive Analytics, Mental Health, Early Detection, Psychiatric Disorders
References
[1] Ahmed, Z., et al. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020(1), 1–13. https://doi.org/10.1093/database/baaa010
[2] Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? The Journal of Arthroplasty, 33(8), 2358–2361. https://doi.org/10.1016/j.arth.2018.02.067
[3] Fitzpatrick, K. K., et al. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785
[4] Gooding, P., & Kariotis, T. (2021). Ethics and law in research on algorithmic and data-driven technology in mental health care: Scoping review. JMIR Mental Health, 8(6), e24668. https://doi.org/10.2196/24668
[5] Graham, S., et al. (2019). Artificial intelligence for mental health and mental illnesses: An overview. Current Psychiatry Reports, 21(11), 116. https://doi.org/10.1007/s11920-019-1094-0
[6] Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 271. https://doi.org/10.3390/ijerph18010271
[7] Nabwire, S., et al. (2021). Review: Application of artificial intelligence in phenomics. Sensors, 21(13), 4363. https://doi.org/10.3390/s21134363
[8] Panch, T., et al. (2018). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2), 020303. https://doi.org/10.7189/jogh.08.020303
[9] Sitaraman, S. R. (2021). AI-driven healthcare systems enhanced by advanced data analytics and mobile computing. International Journal of Information Technology and Computer Engineering, 9(2), 175–187. https://ijitce.org/index.php/ijitce/article/view/229
[10] Jangid, J. (2020). Efficient Training Data Caching for Deep Learning in Edge Computing Networks.
[11] Chukwuebuka, N. a. J. (2022). Distributed machine learning pipelines in multi-cloud architectures: A new paradigm for data scientists. International Journal of Science and Research Archive, 5(2), 357–372. https://doi.org/10.30574/ijsra.2022.5.2.0049
How to cite this paper
@article{1703849,
author = {Fnu Zartashea},
title = {AI in Mental Health: Predictive Analytics for Early Detection and Intervention in Psychiatric Disorders},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {6},
number = {4},
pages = {183-194},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703849.pdf},
abstract = {Artificial intelligence (AI) technology proves useful to mental healthcare through predictive analytics, which identifies early-stage psychiatric disorders. AI uses machine learning algorithms to examine large volumes of clinical data that reveal recognizable indicators of depression, anxiety, and schizophrenia before professional diagnosis. The timely identification through early detection enables medical professionals to start proper treatments, increasing favorable treatment results and lowering long-term medical expenses. Numerous analytical techniques, such as data mining, natural processing, and deep learning methods, are used to model mental health disorders and subsequent risk potentials. Incorporating artificial intelligence into psychiatric care systems provides patients with custom-made therapeutic solutions and continuous health checks. This article examines existing AI implementations in psychiatry to demonstrate their effects while discussing obstacles alongside expected developments that will improve healthcare delivery for patients and mental health service operations.},
keywords = {Artificial Intelligence, Machine Learning, Predictive Analytics, Mental Health, Early Detection, Psychiatric Disorders},
month = {October},
}