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 H...
| Publicado en: | Oncology Nursing Forum Vol. 48; no. 1; pp. 81 - 94 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Oncology Nursing Society
Jan2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147707770&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147707770 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0190535X 4F0 jtl: Oncology Nursing Forum issn: 0190535X maglogo: N pubinfo: dt: Jan2021 vid: 48 iid: 1 pid: 12496 pub: Oncology Nursing Society place: Pittsburgh, Pennsylvania artinfo: ui: 147707770 147707770 147707770 10.1188/21.ONF.81-93 147707770 ppf: 81 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Facial Expression Recognition With Machine Learning and Assessment of Distress in Patients With Cancer. aug: au: Linyan Chen Xiangtian Ma Ning Zhu Heyu Xue Hao Zeng Huaying Chen Xupeng Wang Xuelei Ma affil: graduate student in the Department of Biotherapy in the Cancer Center at the State Key Laboratory of Biotherapy at West China Hospital at Sichuan University and at the Collaborative Innovation Center in Chengdu, both in China sug: subj: Cancer Patients Psychological Distress Evaluation Face Perception Machine Learning Human Academic Medical Centers China Clinical Assessment Tools Scales Psychological Distress Prevention and Control Health Screening Conceptual Framework Inpatients Cancer Care Facilities Sensitivity and Specificity Test-Retest Reliability Confidence Intervals Descriptive Statistics Coefficient alpha Male Female Adult Middle Age Random Sample ROC Curve Data Analysis Software Aged Questionnaires Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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