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Srikanth_Tadisetty_Thesis.pdf (988.8 KB)

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Prediction of Psychosis Using Big Web Data in the United States

Tadisetty, Srikanth
http://orcid.org/0000-0002-4699-2055
http://rave.ohiolink.edu/etdc/view?acc_num=kent1532962079970169

Abstract Details

2018, MS, Kent State University, College of Arts and Sciences / Department of Computer Science.
Posting on the internet, including weblogs or social media, is one of the ways individuals seek for an outlet to express themselves or mental health concerns. For many mental health issues such as psychosis, the timing of detection and treatment is critical; short and long-term outcomes are better when individuals begin treatment close to the onset of psychosis. While the internet offers a positive medium for short term therapy, it is not a face to face therapy session, wherein a trained professional is better able to deduce the root of the problem. Many clinicians are adopting electronic communication to strengthen their therapeutic alliance with their patients. The drawback of psychiatry is that it lacks objectified tests for mental illnesses that would otherwise be present in medicine. Current neuroscience has yet not found genetic markers that can characterize individual mental illnesses. A thought disorder (ThD) which is a widely found symptom in people suffering from schizophrenia, is diagnosed from the level of coherence when the flow of ideas is muddled without word associations. A system that can explore the use of speech analysis for aiding in psychiatric diagnosis is highly desirable and would help early detection and effective treatment results. This thesis introduces a framework – Prediction Onset Prediction System (POPS) - to predict the onset of psychosis based on written language habits. A scrape of a multitude of individual comments is analyzed using a trained psychosis prediction module that is able to predict if an individual is psychotic (based on the semantics) using natural language processing, machine learning techniques and a customized corpus with terms consist with psychotic language tendencies created using speech analysis techniques. The effectiveness of the corpus and its implication in psychosis detection is explored.
Kambiz Ghazinour (Advisor)
80 p.
Computer Science; Health; Mental Health; Psychology; Sociology; Teaching; Technology
Machine Learning; NLP; Natural Language Processing; Web Scrapping; Psychosis; Mental Illness; Mental Health; Twitter; Social Media; Psychosis Dictionary; Crisis Prevention; Mental Health Prediction; Psychological Health; Social Media; Lexical Analysis

Recommended Citations

Citations

  • Tadisetty, S. (2018). Prediction of Psychosis Using Big Web Data in the United States [Master's thesis, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1532962079970169

    APA Style (7th edition)

  • Tadisetty, Srikanth. Prediction of Psychosis Using Big Web Data in the United States. 2018. Kent State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1532962079970169.

    MLA Style (8th edition)

  • Tadisetty, Srikanth. "Prediction of Psychosis Using Big Web Data in the United States." Master's thesis, Kent State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1532962079970169

    Chicago Manual of Style (17th edition)

kent1532962079970169
694
© 2018, all rights reserved.
This open access ETD is published by Kent State University and OhioLINK.