Adibi Peyman, Kalani Simindokht, Zahabi Sayed Jalal, Asadi Homa, Bakhtiar Mohsen, Heidarpour Mohammad Reza, Roohafza Hamidreza, Shahoon Hassan, Amouzadeh Mohammad
Isfahan Gastroenterology and Hepatology Research Center, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran.
Department of Psychology, University of Isfahan, Isfahan 8174673441, Iran.
World J Psychiatry. 2023 Jan 19;13(1):1-14. doi: 10.5498/wjp.v13.i1.1.
An important factor in the course of daily medical diagnosis and treatment is understanding patients' emotional states by the caregiver physicians. However, patients usually avoid speaking out their emotions when expressing their somatic symptoms and complaints to their non-psychiatrist doctor. On the other hand, clinicians usually lack the required expertise (or time) and have a deficit in mining various verbal and non-verbal emotional signals of the patients. As a result, in many cases, there is an emotion recognition barrier between the clinician and the patients making all patients seem the same except for their different somatic symptoms. In particular, we aim to identify and combine three major disciplines (psychology, linguistics, and data science) approaches for detecting emotions from verbal communication and propose an integrated solution for emotion recognition support. Such a platform may give emotional guides and indices to the clinician based on verbal communication at the consultation time.
在日常医疗诊断和治疗过程中,一个重要因素是负责诊治的医生了解患者的情绪状态。然而,患者在向非精神科医生表达躯体症状和诉求时,通常会避免说出自己的情绪。另一方面,临床医生通常缺乏所需的专业知识(或时间),在挖掘患者各种言语和非言语情绪信号方面存在不足。因此,在许多情况下,临床医生和患者之间存在情绪识别障碍,除了躯体症状不同外,所有患者看起来都一样。特别是,我们旨在识别并结合三个主要学科(心理学、语言学和数据科学)从言语交流中检测情绪的方法,并提出一种用于情绪识别支持的综合解决方案。这样一个平台可以在会诊时根据言语交流为临床医生提供情绪指导和指标。