Smart virtual expert system to assist psychiatrists (SVESTAP)

Автор: Udara Srimath S. Samaratunge Arachchillage

Журнал: International Journal of Information Technology and Computer Science @ijitcs

Статья в выпуске: 1 Vol. 10, 2018 года.

Бесплатный доступ

Psychological issues in the world are exponentially growing and the treatment gap is also comparatively high. The main reason would be the shortage of expertise and time-consuming in conventional diagnose process. The main objective of this research is to lower the mental issues treatment gap of professionals or apprentices in the field by creating a virtual expert system to assist psychiatrists. This system diagnoses most common mental disorders such as Depression Disorder, Anxiety Disorder, and Dementia. The proposed expert system can communicate with patients, to identify the current state of the illness. During the conversation, a standard questionnaire is given for the disease verification purpose. The experienced mental health professionals can use this expert system to assist in diagnosing process and the apprentices of the psychology can use this expert system as a training asset.

Еще

Psychiatrists, Expert System, Knowledge base, Ontology, Natural Language Understanding (NLU), Natural Language Generation (NLG), Anxiety, Dementia

Короткий адрес: https://sciup.org/15016228

IDR: 15016228   |   DOI: 10.5815/ijitcs.2018.01.07

Список литературы Smart virtual expert system to assist psychiatrists (SVESTAP)

  • H. P. Selker and A. J. J. Wood, “Industry Influence on Comparative-Effectiveness Research Funded through Health Care Reform,” New England Journal of Medicine, vol. 361, pp. 2595-2597, Dec 2009. doi: 10.1056/NEJMp0910747.
  • A. Demirhan, "Pattern analysis for the neuroimaging based diagnosis of schizophrenia," 2017 25th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey, 2017, pp. 1-4.
  • D. E. Heckerman, E. H. Shortliffe, “From certainty factors to belief networks,” Artificial Intelligentce in Medicine, vol. 4, no. 1, pp. 35-52, Feb 1992.
  • C. WenBin, L. XiaoLing, L. YiJun and F. Yu, "A machine learning algorithm for expert system based on MYCIN model," 2010 2nd International Conference on Computer Engineering and Technology, Chengdu, 2010, pp. V2-262-V2-265.
  • G. Barnett, J.J. Cimino, J.A. Hupp and E. P. Hoffer, “Dxplain: An evolving diagnostic decision-support system,” Journal of the American Medical Association (JAMA), vol. 258, no. 1, pp. 67-74 1987.
  • O. J. Ayangbekun, A. I. Olatunde and F. O. Bankole, “An Expert System for Diagnosis of Blood Disorder,” International Journal of Computer Applications (IJCA), vol. 100, no. 7, pp. 37-40, Aug 2014.
  • C. V. D. Schatz and F. K. Schneider, “Intelligent and Expert Systems in Medicine – A Review,” XVIII Congreso Argentino de Bioingeniería SABI 2011 - VII Jornadas de Ingeniería Clínica Mar del PlataSara, pp. 28 – 30, September, 2011, pp. 326-331.
  • A. T. Owoseni, I. O. Ogundahunsi,"Mobile-Based Fuzzy Expert System for Diagnosing Malaria (MFES)", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.8, No.2, pp.14-22, 2016. DOI: 10.5815/ijieeb.2016.02.02.
  • V. Jain, S. Raheja, "Improving the Prediction Rate of Diabetes using Fuzzy Expert System", International Journal of Information Technology and Computer Science (IJITCS), no. 10, pp. 84 - 91, 2015. DOI: 10.5815 / ijitcs. 2015.10.10.
  • T. W. Wlodarczyk, M.O'Connor, C. Rong, M. Musen, "SWRL-F ¬ A Fuzzy Logic Extension of the Semantic Web Rule Language"Available:http://c4i.gmu.edu/ursw/2010/papers/URSW2010_P1_WlodarczykEtAl.pdf.
  • T. W. Wlodarczyk, M. O'Connor, C. Rong, M. Musen, "SWRL-F¬ A Fuzzy Logic Extension of the Semantic Web Rule Language" Available: http://c4i.gmu.edu/ursw/2010/papers/URSW2010_P1_WlodarczykEtAl.pdf.
  • K. Balachandran and S. Ranathunga, "Domain-Specific Term Extraction for Concept Identification in Ontology Construction," 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Omaha, NE, 2016, pp. 34-41.
  • B. Jafarpour, S. R. Abidi and S. S. R. Abidi, "Exploiting Semantic Web Technologies to Develop OWL-Based Clinical Practice Guideline Execution Engines," in IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 1, pp. 388-398, Jan. 2016. doi: 10.1109/JBHI.2014.2383840
  • S. Sitthithanasakul and N. Choosri, "Application of software requirement engineering for ontology construction," 2017 International Conference on Digital Arts, Media and Technology (ICDAMT), Chiang Mai, 2017, pp. 447-453.
  • M. Bates, “Models of natural language understanding,” Proceedings of the National Academy of Sciences, vol. 92, pp. 9977-9982, Oct 1995.
  • W. G. Lehnert, M. H. Ringle, “Strategies for natural language processing,” New York, Psychology Press, 2014.
  • S. Im, M. Sohn, S. Jeong and H. J. Lee, "Keyword-Based SPARQL Query Generation System to Improve Semantic Tractability on LOD Cloud," 2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Birmingham, 2014, pp. 102-109. doi: 10.1109/IMIS.2014.95
  • I. Abdelaziz; M. R. Al-Harbi; S. Salihoglu; P. Kalnis, "Combining Vertex-centric Graph Processing with SPARQL for Large-scale RDF Data Analytics," in IEEE Transactions on Parallel and Distributed Systems, vol. PP, no. 99, pp. 1-1.
  • A.Merazi, M. Malki, "SQUIREL: Semantic Querying Interlinked OWLS traveling Process Models", International Journal of Information Technology and Computer Science (IJITCS), No. 12, pp. 30 – 39, 2015. DOI: 10.5815 / ijitcs. 2015.12.04.
  • S. Puls, D. Lemcke and H. Worn, "Context-sensitive natural language generation for human readable event logs based on situation awareness in human-robot cooperation," 2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Kuala Lumpur, 2014, pp. 350-355.
  • J. Angst et al, “The HCL-32: Towards a self-assessment tool for hypomanic symptoms in outpatients,” Journal of Affective Disorders, vol. 88, no. 2, pp. 217-233, Oct 2005.
  • N. Dethlefs, "Domain Transfer for Deep Natural Language Generation from Abstract Meaning Representations," in IEEE Computational Intelligence Magazine, vol. 12, no. 3, pp. 18-28, Aug. 2017. doi: 10.1109/MCI.2017.2708558.
Еще
Статья научная