West China Hospital.
University of Electronic Science and Technology of China.
Oncol Nurs Forum. 2021 Jan 4;48(1):81-93. doi: 10.1188/21.ONF.81-93.
To estimate the effectiveness of combining facial expression recognition and machine learning for better detection of distress.
SAMPLE & SETTING: 232 patients with cancer in Sichuan University West China Hospital in Chengdu, China.
METHODS & VARIABLES: The Distress Thermometer (DT) and Hospital Anxiety and Depression Scale (HADS) were used as instruments. The HADS included scores for anxiety (HADS-A), depression (HADS-D), and total score (HADS-T). Distressed patients were defined by the DT cutoff score of 4, the HADS-A cutoff score of 8 or 9, the HADS-D cutoff score of 8 or 9, or the HADS-T cutoff score of 14 or 15. The authors applied histogram of oriented gradients to extract facial expression features from face images, and used a support vector machine as the classifier.
The facial expression features showed feasible differentiation ability on cases classified by DT and HADS.
Facial expression recognition could serve as a supplementary screening tool for improving the accuracy of distress assessment and guide strategies for treatment and nursing.
评估结合面部表情识别和机器学习以提高痛苦检测效果。
在中国成都四川大学华西医院的 232 名癌症患者。
使用痛苦温度计（DT）和医院焦虑和抑郁量表（HADS）作为工具。HADS 包括焦虑评分（HADS-A）、抑郁评分（HADS-D）和总评分（HADS-T）。通过 DT 截断值为 4、HADS-A 截断值为 8 或 9、HADS-D 截断值为 8 或 9 或 HADS-T 截断值为 14 或 15 来定义痛苦患者。作者应用方向梯度直方图从面部图像中提取面部表情特征，并使用支持向量机作为分类器。
面部表情特征在 DT 和 HADS 分类的病例中表现出可行的区分能力。
面部表情识别可以作为一种补充筛查工具，以提高痛苦评估的准确性，并指导治疗和护理策略。
