Development of Breast Cancer Prognosis Prediction Model Based on Clinical Features Including CEA and CA15-3 Serum Levels
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Objectives: This study investigated psychosocial factors that influence nurse’s intention to receive a COVID-19 vaccine. We focused on factors including COVID-19 knowledge, attitudes and beliefs related to COVID-19 vaccines, social contexts, and demographics. Methods: Data were collected from 200 nurses at five hospitals in Daegu between 12 April and 19 June 2021. We assessed COVID-19 knowledge, perceived susceptibility, severity, benefits, barriers, safety concerns, COVID-19 exposure, demographics, and the intention to get COVID-19 vaccines. Data were analyzed using descriptive statistics, t-tests, ANOVA, Pearson correlation, and hierarchial multiple regression. Results: Of 200 participants, 180 (90.0%) reported an intention to obtain a vaccination against COVID-19. Among four health beliefs towards COVID-19 vaccines, perceived severity was the highest. Perceived benefits (r = 0.42, p< 0.001) were positively associated with vaccination intention, while perceived barriers (r = -0.32, p< 0.001) and safety concerns (r = -0.20, p< 0.001) negatively influenced vaccination intention. There were differences in the scores for vaccination intention according to hospital grade and work department. According to the regression analysis, perceived benefits and barriers were discovered to account for 21% of the variance in vaccination intention. Conclusions: This study offers a timely overview of psychosocial factors that are related to nurses’ intention to get vaccinated for COVID-19. The vaccines’ benefits need to be highlighted in campaigns and educations, and it is imperative to remain transparent and truthful in communications about the vaccine safety.
Abstract
Objectives: This study investigated psychosocial factors that influence nurse’s intention to receive a COVID-19 vaccine. We focused on factors including COVID-19 knowledge, attitudes and beliefs related to COVID-19 vaccines, social contexts, and demographics. Methods: Data were collected from 200 nurses at five hospitals in Daegu between 12 April and 19 June 2021. We assessed COVID-19 knowledge, perceived susceptibility, severity, benefits, barriers, safety concerns, COVID-19 exposure, demographics, and the intention to get COVID-19 vaccines. Data were analyzed using descriptive statistics, t-tests, ANOVA, Pearson correlation, and hierarchial multiple regression. Results: Of 200 participants, 180 (90.0%) reported an intention to obtain a vaccination against COVID-19. Among four health beliefs towards COVID-19 vaccines, perceived severity was the highest. Perceived benefits (r = 0.42, p< 0.001) were positively associated with vaccination intention, while perceived barriers (r = -0.32, p< 0.001) and safety concerns (r = -0.20, p< 0.001) negatively influenced vaccination intention. There were differences in the scores for vaccination intention according to hospital grade and work department. According to the regression analysis, perceived benefits and barriers were discovered to account for 21% of the variance in vaccination intention. Conclusions: This study offers a timely overview of psychosocial factors that are related to nurses’ intention to get vaccinated for COVID-19. The vaccines’ benefits need to be highlighted in campaigns and educations, and it is imperative to remain transparent and truthful in communications about the vaccine safety.
참고문헌 (47)
- Boeri C, "Machine learning techniques in breast cancer prognosis prediction : a primary evaluation", Cancer Med, 9(9), pp.3234~3243 (2020)[DOI]
- Stark GF, "Predicting breast cancer risk using personal health data and machine learning models", PLoS One, 14(12), pp.e0226765~ (2019)[DOI]
- Woojae Kim, "Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine", Journal of Breast Cancer, 15(2), pp.230~238 (2012), 한국유방암학회[DOI]
- Chatterjee SK, "Cancer biomarkers : knowing the present and predicting the future", Future Oncol, 1(1), pp.37~50 (2005)[DOI]
- Wang J, "Combined detection of preoperative serum CEA, CA19-9 and CA242 improve prognostic prediction of surgically treated colorectal cancer patients", Int J Clin Exp Pathol, 8(11), pp.14853~14863 (2015)[DOI]
- Hayes DF, "Comparison of circulating CA15-3 and carcinoembryonic antigen levels in patients with breast cancer", J Clin Oncol, 4(10), pp.1542~1550 (1986)[DOI]
- O’Hanlon DM, "Tissue polypeptide specific antigen(TPS)in breast cancer--an initial evaluation", Eur J Surg Oncol, 22(1), pp.38~41 (1996)[DOI]
- Vizcarra E, "CA 15. 3, CEA and TPA tumor markers in the early diagnosis of breast cancer relapse", Oncology, 51(6), pp.491~496 (1994)[DOI]
- Van Dalen A, "The prognostic significance of increasing marker levels in metastatic breast cancer patients with clinically complete remission, partial remission or stable disease", Int J Biol Markers, 13(1), pp.10~15 (1998)[DOI]
- Peng Y, "Role of blood tumor markers in predicting metastasis and local recurrence after curative resection of colon cancer", Int J Clin Exp Med, 8(1), pp.982~990 (2015)
- Van Dalen A, "Treatment response in metastatic breast cancer. A multicenter study comparing UICC criteria and tumor marker changes", The Breast, 5(2), pp.82~87 (1996)[DOI]
- Lin JK, "Early postoperative CEA level is a better prognostic indicator than is preoperative CEA level in predicting prognosis of patients with curable colorectal cancer", Int J Colorectal Dis, 26(9), pp.1135~1141 (2011)[DOI]
- Díez M, "Prediction of survival and recurrence by serum and cytosolic levels of CEA, CA125 and SCC antigens in resectable non-small-cell lung cancer", Br J Cancer, 73(10), pp.1248~1254 (1996)[DOI]
- Wiwiek Poedjiastoeti, "Application of Convolutional Neural Network in the Diagnosis of Jaw Tumors", Healthcare Informatics Research, 24(3), pp.236~241 (2018), 대한의료정보학회[DOI]
- Wiharto Wiharto, "Interpretation of Clinical Data Based on C4.5 Algorithm for the Diagnosis of Coronary Heart Disease", Healthcare Informatics Research, 22(3), pp.186~195 (2016), 대한의료정보학회[DOI]
- Chawla NV, "Editorial : special issue on learning from imbalanced data sets", ACM SIGKDD Exploration Newsletter, 6(1), pp.1~6 (2004)[DOI]
- Thanathamathee P, "Handling imbalanced data sets with synthetic boundary data generation using bootstrap re-sampling and AdaBoost techniques", Pattern Recogn Lett, 34(12), pp.1339~1347 (2013)[DOI]
- Park BW, "Preoperative CA15-3 and CEA serum levels as predictor for breast cancer outcomes", Ann Oncol, 19(4), pp.675~681 (2008)[DOI]
- Kotsiantis SB, "Data preprocessing for supervised leaning", Int J Comput Sci, 1(1), pp.111~117 (2006)[DOI]
- Ioffe S, "Batch normalization: Accelerating deep network training by reducing internal covariate shift", arXiv 2015;37:448-456. DOI: 10.5555, 2015
- Yadav ML, "Handling missing values : a study of popular imputation packages in R", Knowl Based Syst, 160, pp.104~118 (2018)[DOI]
- Elreedy D, "A comprehensive analysis of synthetic minority oversampling technique(SMOTE)for handling class imbalance", Inf Sci, 505, pp.32~64 (2019)[DOI]
- Efron B., "Breakthroughs in statistics", pp.569~593 (1992), Springer
- Moody F, "Fast learning in networks of locally-tuned processing units", Neural Comput, 1(2), pp.281~294 (1989)[DOI]
- Dwivedi AK, "Performance evaluation of different machine learning techniques for prediction of heart disease", Neural Comput Appl, 29, pp.685~693 (2016)[DOI]
- Ratner B, "Statistical and machine-learning data mining: techniques for better predictive modeling and analysis of big data", pp.2017, Chapman and Hall
- Islam MM, "Breast cancer prediction : a comparative study using machine learning techniques", SN Comput Sci, 1, pp.90~ (2020)[DOI]
- James G, "An introduction to statistical learning", pp.103~ (2013), Springer
- Guido S, "Introduction to machine learning with python" (2016), O’Reilly Media Inc
- de Boves Harrington P, "Support vector machine classification trees", Anal Chem, 87(21), pp.11065~11071 (2015)[DOI]
- Lorena AC, "Comparing machine learning classifiers in potential distribution modelling", Expert Syst Appl, 38(5), pp.5268~5275 (2011)[DOI]
- Nie F, "Decision Tree SVM : An extension of linear SVM for non-linear classification", Neurocomputing, 401, pp.153~159 (2020)[DOI]
- Stehman SV, "Selecting and interpreting measures of thematic classification accuracy", Remote Sens Environ, 62(1), pp.77~89 (1997)[DOI]
- Powers DM, "Evaluation : from precision, recall and F-measure to ROC, informedness, markedness and correlation", Int J Mach Learn Technol, 2, pp.37~63 (2011)
- Lundberg SM, "A unified approach to interpreting model predictions", Adv Neural Inf Process Syst, pp.4765~4774 (2017)
- Park JH, "A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea", BMC Med Inform Decis Mak, 20, pp.241~ (2020)[DOI]
- Jin Hyang Jung, "Clinical Value of CEA, CA15-3 and TPS in Breast Cancer", Journal of Korean Breast Cancer Society, 4(2), pp.136~ (2001), Korean Breast Cancer Society[DOI]
- Giai M, "TPS and CA 15.3 serum values as a guide for treating and monitoring breast cancer patients", Anticancer Res, 16(2), pp.875~881 (1996)
- Ceylan Z, "Diagnosis of breast cancer using improved machine learning algorithms based on Bayesian optimization", Int J Intell Sys Appl, 8(3), pp.121~130 (2020)[DOI]
- Hwa HL, "Prediction of breast cancer and lymph node metastatic status with tumour markers using logistic regression models", J Eval Clin Pract, 14(2), pp.275~280 (2008)[DOI]
- Molina R, "Use of serial carcinoembryonic antigen and CA 15-3 assays in detecting relapses in breast cancer patients", Breast Cancer Res Treat, 36, pp.41~48 (1995)[DOI]
- Covemey EC, "The clinical value of CEA and CA 15-3 in breast cancer management", Int J BioI Markers, 10(1), pp.35~41 (1995)
- Haraguchi M, "The serum level of carcinoembryonic antigen in drainage venous blood is not a sensitive predictor of metachronous hepatic metastasis for patients with colorectal cancer", Surg Today, 40(8), pp.745~751 (2010)[DOI]
- Benjapibal M, "Pre-operative prediction of serum CA125level in women with ovarian masses", J Med Assoc Thai, 90(10), pp.1986~1991 (2007)
- Giordano SH, "Breast cancer treatment guidelines in older women", J Clin Oncol, 23(4), pp.783~791 (2005)[DOI]
- Tseng YJ, "Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies", Int J Med Inform, 128, pp.79~86 (2019)[DOI]
- Ebeling FG, "Serum CEA and CA 15-3 as prognostic factors in primary breast cancer", Br J Cancer, 86(8), pp.1217~1222 (2002)[DOI]
