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- Title
Facial Expression Recognition With Machine Learning and Assessment of Distress in Patients With Cancer.
- Authors
Linyan Chen; Xiangtian Ma; Ning Zhu; Heyu Xue; Hao Zeng; Huaying Chen; Xupeng Wang; Xuelei Ma
- Abstract
OBJECTIVES: 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. RESULTS: The facial expression features showed feasible differentiation ability on cases classified by DT and HADS. IMPLICATIONS FOR NURSING: Facial expression recognition could serve as a supplementary screening tool for improving the accuracy of distress assessment and guide strategies for treatment and nursing.
- Subjects
CHINA; ACADEMIC medical centers; CANCER patients; CANCER treatment; CONCEPTUAL structures; CONFIDENCE intervals; STATISTICAL correlation; PSYCHOLOGICAL distress; MACHINE learning; MEDICAL screening; QUESTIONNAIRES; STATISTICAL sampling; STATISTICAL reliability; SPECIALTY hospitals; RECEIVER operating characteristic curves; DATA analysis software; DESCRIPTIVE statistics
- Publication
Oncology Nursing Forum, 2021, Vol 48, Issue 1, p81
- ISSN
0190-535X
- Publication type
Article
- DOI
10.1188/21.ONF.81-93