Quantifiable Structured Clinical Diagnosis for Psychiatry: An Integration of Machine Learning and Cloud Computing Approaches to Achieve Scalability
Date
2022-05-11
Authors
Laith Azzam Ayasa
Matthew Toegel
Joman Y. Natsheh
Mohmmad M. Herzallah
Journal Title
Journal ISSN
Volume Title
Publisher
Al-Quds University, Deanship of Scientific Research
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.