Abstract
In the domain of psychological practice, experts follow different methodologies for the diagnosis of psychological disorders and might change their line of treatment based on their observations from previous sessions. In such a scenario, a standardized clinical decision support system based on big data and machine learning techniques can immensely help professionals in the process of diagnosis as well as improve patient care. The technology proposed in this paper, attempts to understand psychological case studies by identifying the psychological disorder they represent along with the severity of that particular case, with the help of a Multinomial Naive Bayes model for disorder identification and a regular expression based severity processing algorithm. A knowledge base is created based on the knowledge of human experts of psychology. Psychological disorders however need not possess distinct symptoms to easily differentiate between them. Some are very closely connected with a variety of overlapping symptoms between them. Our work, in this paper, focuses on analyzing the performance of such psychological disorders represented in the form of case studies in a decision support system, with an aim of understanding this gray area of psychology.
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References
Apache mahout: Scalable machine learning and data mining. https://mahout.apache.org/. Accessed 1 May 2018
JMIR mental health. https://mental.jmir.org/. Accessed 1 May 2018
Living with schizophrenia. https://www.livingwithschizophreniauk.org/. Accessed 1 May 2018
OMICS International. https://www.omicsonline.org/. Accessed 1 May 2018
Paranoia - better health channel. https://www.betterhealth.vic.gov.au/health/conditionsandtreatments/paranoia. Accessed 1 May 2018
American Psychiatric Association, et al.: Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub (2013)
Casado-Lumbreras, C., Rodríguez-González, A., Álvarez-Rodríguez, J.M., Colomo-Palacios, R.: PsyDis: towards a diagnosis support system for psychological disorders. Expert Syst. Appl. 39(13), 11391–11403 (2012)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis, vol. 3. Wiley, New York (1973)
Lyubimov, D., Palumbo, A.: Apache Mahout: Beyond MapReduce. CreateSpace Independent Publishing Platform (2016)
Masri, R.Y., Jani, H.M.: Employing artificial intelligence techniques in mental health diagnostic expert system. In: 2012 International Conference on Computer and Information Science (ICCIS), vol. 1, pp. 495–499. IEEE (2012)
Miklowitz, D.J., Gitlin, M.J.: Clinician’s Guide to Bipolar Disorder: Integrating Pharmacology and Psychotherapy. Guilford Publications (2014)
Sumathi, M.R., Poorna, B.: Prediction of mental health problems among children using machine learning techniques. Int. J. Adv. Comput. Sci. Appl. 7(1), 552–557 (2016)
Razzouk, D., Mari, J.J., Shirakawa, I., Wainer, J., Sigulem, D.: Decision support system for the diagnosis of schizophrenia disorders. Braz. J. Med. Biol. Res. 39(1), 119–128 (2006)
Rennie, J.D., Shih, L., Teevan, J., Karger, D.R.: Tackling the poor assumptions of Naive Bayes text classifiers. In: Proceedings of the 20th International Conference on Machine Learning (Icml-03), pp. 616–623 (2003)
Suhasini, A., Palanivel, S., Ramalingam, V.: Multimodel decision support system for psychiatry problem. Expert Syst. Appl. 38(5), 4990–4997 (2011)
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Bhattacharjee, K. et al. (2020). Performance Analysis of Psychological Disorders for a Clinical Decision Support System. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_87
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DOI: https://doi.org/10.1007/978-3-030-16660-1_87
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