Quantifiable Structured Clinical Diagnosis for Psychiatry: An Integration of Machine Learning and Cloud Computing Approaches to Achieve Scalability

dc.contributor.author	Laith Azzam Ayasa
dc.contributor.author	Matthew Toegel
dc.contributor.author	Joman Y. Natsheh
dc.contributor.author	Mohmmad M. Herzallah
dc.date.accessioned	2022-06-22T11:46:44Z
dc.date.available	2022-06-22T11:46:44Z
dc.date.issued	2022-05-11
dc.description.abstract	Background: Current diagnostic systems for psychiatric disorders suffer many limitations that hinder their applicability. The diagnosis of psychiatric disorders is exclusively conducted by clinicians using lengthy interviews that lack sensitivity and specificity. According to recent clinical trials, only a fraction of patients with psychiatric disorders respond to initial treatment with psychometric medications or psychotherapy. Unfortunately, clinicians cannot predict, a priori, who will or will not respond to treatment. If, however, a simple computer-based system utilizing multidimensional symptom expression could diagnose patients with psychiatric disorders and differentiate those who are, or are not, likely to respond to treatment, this would provide immediate clinical relevance.
dc.identifier.uri	https://hdl.handle.net/20.500.12213/6592
dc.language.iso	en
dc.publisher	Al-Quds University, Deanship of Scientific Research
dc.title	Quantifiable Structured Clinical Diagnosis for Psychiatry: An Integration of Machine Learning and Cloud Computing Approaches to Achieve Scalability
dc.type	Article
dspace.entity.type

Now showing 1 - 1 of 1

Name:: Quantifiable Structured Clinical Diagnosis for Psychiatry An Integration of Machine Learning and Cloud Computing Approaches to Achieve Scalability.pdf
Size:: 311.15 KB
Format:: Adobe Portable Document Format
Description:

Now showing 1 - 1 of 1