Barzilay Ran, Israel Nadav, Krivoy Amir, Sagy Roi, Kamhi-Nesher Shiri, Loebstein Oren, Wolf Lior, Shoval Gal
Geha Mental Health Center, Petach Tikva, Israel.
Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
Front Psychiatry. 2019 May 6;10:288. doi: 10.3389/fpsyt.2019.00288. eCollection 2019.
Classifying patients' affect is a pivotal part of the mental status examination. However, this common practice is often widely inconsistent between raters. Recent advances in the field of Facial Action Recognition (FAR) have enabled the development of tools that can act to identify facial expressions from videos. In this study, we aimed to explore the potential of using machine learning techniques on FAR features extracted from videotaped semi-structured psychiatric interviews of 25 male schizophrenia inpatients (mean age 41.2 years, STD = 11.4). Five senior psychiatrists rated patients' affect based on the videos. Then, a novel computer vision algorithm and a machine learning method were used to predict affect classification based on each psychiatrist affect rating. The algorithm is shown to have a significant predictive power for each of the human raters. We also found that the eyes facial area contributed the most to the psychiatrists' evaluation of the patients' affect. This study serves as a proof-of-concept for the potential of using the machine learning FAR system as a clinician-supporting tool, in an attempt to improve the consistency and reliability of mental status examination.
对患者的情感进行分类是精神状态检查的关键部分。然而，这种常见做法在评估者之间往往存在很大差异。面部动作识别（FAR）领域的最新进展使得能够开发出从视频中识别面部表情的工具。在本研究中，我们旨在探索对从25名男性精神分裂症住院患者（平均年龄41.2岁，标准差=11.4）的录像半结构化精神科访谈中提取的FAR特征使用机器学习技术的潜力。五位资深精神科医生根据视频对患者的情感进行评分。然后，使用一种新颖的计算机视觉算法和机器学习方法根据每位精神科医生的情感评分来预测情感分类。结果表明，该算法对每位人类评估者都具有显著的预测能力。我们还发现，眼睛面部区域对精神科医生评估患者情感的贡献最大。本研究为使用机器学习FAR系统作为临床医生辅助工具的潜力提供了概念验证，旨在提高精神状态检查的一致性和可靠性。
