判别式
概化理论
接收机工作特性
精神分裂症(面向对象编程)
精神分裂症的诊断
曲线下面积
听力学
精神科
医学
心理学
精神病
临床心理学
内科学
机器学习
发展心理学
计算机科学
作者
Shih‐Chieh Lee,Chen-Chung Liu,Cheng‐Deng Kuo,I-Ping Hsueh,Ching-Lin Hsieh
标识
DOI:10.1016/j.jad.2020.07.003
摘要
Schizophrenia is a debilitating mental illness that causes significant disability. However, the lack of evidence for functional decline yields difficulty in distinguishing patients with schizophrenia from healthy adults. Since patients with schizophrenia demonstrate severe facial emotion recognition deficit (FERD), FERD measurement appears to be a promising solution for the aforementioned challenge.We aimed to develop a FERD-based screening tool to differentiates patients with schizophrenia from healthy adults. Patients' responses were extracted from a previous study. The most discriminative index was determined by comparing the area under the receiver operating characteristic curve (AUC) of patients’ FER scores in 7 domains individually and collectively. The best cut-off score was selected only for the most discriminative index to provide both high sensitivity and specificity (≥ 0.90). The “number of domains failed” showed the highest discriminative value (AUC = 0.92). Since high sensitivity and specificity could not be achieved simultaneously, two sub-optimal cut-off scores were recommended for prospective users. For users prioritizing sensitivity, the “≥ 2 domains failed” index yields high sensitivity (0.96) with modest specificity (0.66). For users targeting specificity, the “≥ 4 domains failed” indexachieves high specificity (0.92) with acceptable sensitivity (0.72). Convenience sampling with mild clinical severity and younger healthy adults (< 20 years old) may limit the generalizability. The FERD screener seems to be a discriminative tool with changeable cut-off scores achieving high sensitivity or specificity. Therefore, it may be useful in detecting patients and ruling out adults erroneously suspected of having schizophrenia.
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