首页|Analysis of Clinicians’ Subjective Approach Based on Three-way Mental Disorders Classification

Analysis of Clinicians’ Subjective Approach Based on Three-way Mental Disorders Classification

扫码查看
The traditional psychiatric diagnosis process is based on psychiatric nosology or mental disorders classification that follows a data-driven objective approach which implies an internal information-based analysis that is found unable in solving many complex cases. To construct a unified framework while in the psychiatric diagnosis process, as well as with other criteria, similar importance should be imposed on practitioners’ external analysis consisting of culture-specific knowledge along with domain knowledge, through attained expertise and experience with a subjective approach. Based on the principles of three-way decision, this thesis concentrated on the subjective approach from the clinician and proposed a model for the clinician’s subjective approach (CSA) analysis with two steps. The first step is qualitative and quantitative analysis. In the qualitative approach, we talked about the property of trichotomy, which plays an essential role in representing user preference. By building a structure using the TAO model, we can rank a set of disorders concerning their importance. In the quantitative approach, the three-level computing model is adopted. Numerical weights are assigned to disorders using the eigenvector method. The second step is utilizing the evaluation-based three-way decision to categorize a disorder set into three classes of different importance in advance. We use three-way decision theory as our fundamental framework to examine CSA to classify mental disorders in psychiatric diagnosis. This CSA model might be integrated with the traditional psychiatric diagnosis set up as an effective tool to make the whole diagnostic process more robust and powerful.

Md Sakib Ullah Sourav

展开 >

Clinicians Subjective Approach Mental Disorders Three-way Decision

硕士

Management Science and Engineering

Huidong Wang

2023

山东财经大学

中文

R1