Facial Expression Recognition With Machine Learning and Assessment of Distress in Patients With Cancer.

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.