Suicidal risk evaluation using a similarity-based classifier
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal Volume
5139 LNAI
Pages
51-61
Date Issued
2008
Author(s)
Chattopadhyay S.
Ray P.
Chen H.S.
MING-BEEN LEE
Chiang H.C.
Abstract
Suicide remains one of the leading causes of death in the world and it is showing an increasing trend. Suicide is preventable by early screening of the risks. But the risk assessment is a complex task due to involvement of multiple predictors, which are highly subjective in nature and varies from one case to another. Moreover, none of the available suicide intent scales (SIS) are found to be sufficient to evaluate the risk patterns in a group of patients. Given this scenario, the present paper applies similarity-based pattern-matching technique for mining suicidal risks in vulnerable groups of patients. At first, medical data of groups of suicidal patients have been collected and modeled according to Pierce's Suicide Intent Scale (PSIS) and then engineered using a JAVA-based pattern-matching tool that performs as an intelligent classifier. Results show that addition of more factors, for example, age and sex of the patients brings more clarity to identify the suicidal risk patterns. ? 2008 Springer-Verlag Berlin Heidelberg.
Subjects
JAVA platform; Pierce's Suicidal Intent Scale (SIS); Similarity-based approach; Suicidality; Suicide risk assessment
SDGs
Other Subjects
Diagnosis; Java programming language; Pattern matching; Risk assessment; Risk perception; Causes of death; Intelligent classifiers; Java platforms; Pattern-matching technique; Risk evaluation; Similarity-based approach; Suicidality; Vulnerable groups; Data mining
Type
conference paper