Exploration of a Machine Learning Model Using Self-rating Questionnaires for Detecting Depression in Patients with Breast Cancer
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier. The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively. The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion: The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.
Abstract
Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier. The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively. The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion: The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.
참고문헌 (35)
- Stein KD, "Physical and psychological long-term and late effects of cancer", Cancer, 112(11 Suppl), pp.2577~2592 (2008)
- Krebber AM, "Prevalence of depression in cancer patients : a meta-analysis of diagnostic interviews and self-report instruments", Psychooncology, 23, pp.121~130 (2014)
- Love AW, "Screening for depression in women with metastatic breast cancer : a comparison of the Beck Depression Inventory short form and the Hospital Anxiety and Depression Scale", Aust N Z J Psychiatry, 38, pp.526~531 (2004)
- Purkayastha D, "Prevalence of depression in breast cancer patients and its association with their quality of life : a cross-sectional observational study", Indian J Palliat Care, 23, pp.268~273 (2017)
- Hwang KS, "Effects of psychosocial interventions for patients with breast cancer : a meta-analysis", Clin Psychopharmacol Neurosci, 21, pp.118~125 (2023)
- Eskelinen M, "The relation of hopelessness", helplessness versus Beck Depression Inventory(BDI)in healthy individuals and in patients with benign breast disease and breast cancer : a prospective case-control study in Finland, 2015, pp.35, 941~947
- Kissane DW, "Unrecognised and untreated depression in cancer care", Lancet Psychiatry, 1, pp.320~321 (2014)
- Giese-Davis J, "Decrease in depression symptoms is associated with longer survival in patients with metastatic breast cancer : a secondary analysis", J Clin Oncol, 29, pp.413~420 (2011)
- Xu C, "Prognostic value of negative emotions on the incidence of breast cancer : a systematic review and meta-analysis of 129, 621 patients with breast cancer", Cancers(Basel), 14, pp.475~ (2022)
- Park H, "Psychometric properties of assessment tools for depression, anxiety, distress, and psychological problems in breast cancer patients : a systematic review", Psychiatry Investig, 20, pp.395~407 (2023)
- Beck AT, "Internal consistencies of the original and revised Beck Depression Inventory", J Clin Psychol, 40, pp.1365~1367 (1984)
- Beck AT, "Screening for major depression disorders in medical inpatients with the Beck Depression Inventory for Primary Care", Behav Res Ther, 35, pp.785~791 (1997)
- Eskelinen M, "Beck Depression Inventory(BDI)in patients with breast disease and breast cancer : a prospective case-control study", In Vivo, 25, pp.111~116 (2011)
- Vodermaier A, "Screening for emotional distress in cancer patients : a systematic review of assessment instruments", J Natl Cancer Inst, 101, pp.1464~1488 (2009)
- Stafford L, "Screening for depression and anxiety in women with breast and gynaecologic cancer : course and prevalence of morbidity over 12 months", Psychooncology, 22, pp.2071~2078 (2013)
- Saboonchi F, "Examination of the construct validity of the Swedish version of Hospital Anxiety and Depression Scale in breast cancer patients", Qual Life Res, 22, pp.2849~2856 (2013)
- Tomljenović H, "Hospital Anxiety and Depression Scale : psychometric validation on a sample of croatian breast cancer patients", Croat J Oncol, 49(Suppl), pp.101~102 (2021)
- Hajian-Tilaki K, "Factor structure and reliability of Persian version of Hospital Anxiety and Depression Scale in patients with breast cancer survivors", Health Qual Life Outcomes, 18, pp.176~ (2020)
- Kroenke K, "The PHQ-9 : validity of a brief depression severity measure", J Gen Intern Med, 16, pp.606~613 (2001)
- An JY, "Standardization of the Korean version of screening tool for depression(Patient Health Questionnaire-9, PHQ-9)", J Korean Soc Biol Ther Psychiatry, 19, pp.47~56 (2013)
- Kim J, "Depression screening using daily mental-health ratings from a smartphone application for breast cancer patients", J Med Internet Res, 18, pp.e216~ (2016)
- Torres A, "Preliminary psychometric characteristics of the Portuguese version of Patient Health Questionnaire 9(PHQ-9)in a sample of Portuguese breast cancer women", Eur Psychiatry, 28(Suppl 1), pp.1~ (2013)
- Roth AJ, "Rapid screening for psychologic distress in men with prostate carcinoma : a pilot study", Cancer, 82, pp.1904~1908 (1998)
- Bidstrup PE, "Accuracy of the Danish version of the ‘Distress Thermometer’", Psychooncology, 21, pp.436~443 (2012)
- Yong HW, "Validation of Malaysian translated Distress Thermometer with problem checklist among the breast cancer survivors in Malaysia", Asian J Psychiatr, 5, pp.38~42 (2012)
- Iskandarsyah A, "The Distress Thermometer and its validity : a first psychometric study in Indonesian women with breast cancer", PLoS One, 8, pp.e56353~ (2013)
- Civilotti C, "The use of the Distress Thermometer and the Hospital Anxiety and Depression Scale for screening of anxiety and depression in Italian women newly diagnosed with breast cancer", Support Care Cancer, 28, pp.4997~5004 (2020)
- Lim JS, "Prediction models for suicide attempts among adolescents using machine learning techniques", Clin Psychopharmacol Neurosci, 20, pp.609~620 (2022)
- Zigmond AS, "The Hospital Anxiety and Depression Scale", Acta Psychiatr Scand, 67, pp.361~370 (1983)
- Lisspers J, "Hospital Anxiety and Depression Scale(HAD) : some psychometric data for a Swedish sample", Acta Psychiatr Scand, 96, pp.281~286 (1997)
- Malasi TH, "Validation of the Hospital Anxiety and Depression Scale in Arab patients", Acta Psychiatr Scand, 84, pp.323~326 (1991)
- Lee KS, "Machine learning on early diagnosis of depression", Psychiatry Investig, 19, pp.597~605 (2022)
- Bhadra S, "An insight into diagnosis of depression using machine learning techniques : a systematic review", Curr Med Res Opin, 38, pp.749~771 (2022)
- Annunziata MA, "Hospital Anxiety and Depression Scale(HADS)accuracy in cancer patients", Support Care Cancer, 28, pp.3921~3926 (2020)
- Affleck AG, "The Hospital Anxiety and Depression Scale is a screening measure of general distress", Br J Dermatol, 179, pp.544~ (2018)