Journal of Korean Academy of Nursing

In Sook Cho

4 Articles

Predictive Bayesian Network Model Using Electronic Patient Records for Prevention of Hospital-Acquired Pressure Ulcers: In Sook Cho, Eunja Chung; J Korean Acad Nurs 2011;41(3):423-431. Published online June 13, 2011; DOI: https://doi.org/10.4040/jkan.2011.41.3.423

Purpose

The study was designed to determine the discriminating ability of a Bayesian network (BN) for predicting risk for pressure ulcers.

Methods

Analysis was done using a retrospective cohort, nursing records representing 21,114 hospital days, 3,348 patients at risk for ulcers, admitted to the intensive care unit of a tertiary teaching hospital between January 2004 and January 2007. A BN model and two logistic regression (LR) versions, model-I and -II, were compared, varying the nature, number and quality of input variables. Classification competence and case coverage of the models were tested and compared using a threefold cross validation method.

Results

Average incidence of ulcers was 6.12%. Of the two LR models, model-I demonstrated better indexes of statistical model fits. The BN model had a sensitivity of 81.95%, specificity of 75.63%, positive and negative predictive values of 35.62% and 96.22% respectively. The area under the receiver operating characteristic (AUROC) was 85.01% implying moderate to good overall performance, which was similar to LR model-I. However, regarding case coverage, the BN model was 100% compared to 15.88% of LR.

Conclusion

Discriminating ability of the BN model was found to be acceptable and case coverage proved to be excellent for clinical use.

Citations

Citations to this article as recorded by

Evaluation of the risk prediction model of pressure injuries in hospitalized patient: A systematic review and meta‐analysis
Yuxia Ma, Xiang He, Tingting Yang, Yifang Yang, Ziyan Yang, Tian Gao, Fanghong Yan, Boling Yan, Juan Wang, Lin Han
Journal of Clinical Nursing.2025; 34(6): 2117. CrossRef
Using nursing data for machine learning-based prediction modeling in intensive care units: A scoping review
Yesol Kim, Mihui Kim, Yeonju Kim, Mona Choi
International Journal of Nursing Studies.2025; 169: 105133. CrossRef
Development of a Pressure Injury Machine Learning Prediction Model and Integration into Clinical Practice: A Prediction Model Development and Validation Study
Ju Hee Lee, Jae Yong Yu, So Yun Shim, Kyung Mi Yeom, Hyun A Ha, Se Yong Jekal, Ki Tae Moon, Joo Hee Park, Sook Hyun Park, Jeong Hee Hong, Mi Ra Song, Won Chul Cha
Korean Journal of Adult Nursing.2024; 36(3): 191. CrossRef
The predictive effect of different machine learning algorithms for pressure injuries in hospitalized patients: A network meta-analyses
Chaoran Qu, Weixiang Luo, Zhixiong Zeng, Xiaoxu Lin, Xuemei Gong, Xiujuan Wang, Yu Zhang, Yun Li
Heliyon.2022; 8(11): e11361. CrossRef
Predictive Modeling of Pressure Injury Risk in Patients Admitted to an Intensive Care Unit
Mireia Ladios-Martin, José Fernández-de-Maya, Francisco-Javier Ballesta-López, Adrián Belso-Garzas, Manuel Mas-Asencio, María José Cabañero-Martínez
American Journal of Critical Care.2020; 29(4): e70. CrossRef
Development and Evaluation of Electronic Health Record Data-Driven Predictive Models for Pressure Ulcers
Seul Ki Park, Hyeoun-Ae Park, Hee Hwang
Journal of Korean Academy of Nursing.2019; 49(5): 575. CrossRef
Development and Comparison of Predictive Models for Pressure Injuries in Surgical Patients
Seul Ki Park, Hyeoun-Ae Park, Hee Hwang
Journal of Wound, Ostomy & Continence Nursing.2019; 46(4): 291. CrossRef
Automated Pressure Injury Risk Assessment System Incorporated Into an Electronic Health Record System
Yinji Jin, Taixian Jin, Sun-Mi Lee
Nursing Research.2017; 66(6): 462. CrossRef
Recommendation of Personalized Surveillance Interval of Colonoscopy via Survival Analysis
Jayeon Gu, Eun Sun Kim, Seoung Bum Kim
Journal of Korean Institute of Industrial Engineers.2016; 42(2): 129. CrossRef
Medical Data Based Clinical Pathway Analysis and Automatic Ganeration System
Hanna Park, In Ho Bae, Yong Oock Kim
The Journal of Korea Information and Communications Society.2014; 39C(6): 497. CrossRef
Reusability of EMR Data for Applying Cubbin and Jackson Pressure Ulcer Risk Assessment Scale in Critical Care Patients
Eunkyung Kim, Mona Choi, JuHee Lee, Young Ah Kim
Healthcare Informatics Research.2013; 19(4): 261. CrossRef
Using EHR data to predict hospital-acquired pressure ulcers: A prospective study of a Bayesian Network model
Insook Cho, Ihnsook Park, Eunman Kim, Eunjoon Lee, David W. Bates
International Journal of Medical Informatics.2013; 82(11): 1059. CrossRef

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Changes on Index of Nausea, Vomiting, and Retching in Hospitalized Cancer Patients Undergoing Chemotherapy: Young Jae Kim, In Sook Cho, Hyang Sook So; Journal of Korean Academy of Nursing 2004;34(7):1326-1333. Published online March 28, 2017; DOI: https://doi.org/10.4040/jkan.2004.34.7.1326

Abstract PDF

Purpose

The purpose of this study was to determine the changes on Index of Nausea, Vomiting, & Retching (INVR) during a cycle of chemotherapy.

Methods

Forty-three patients hospitalized for chemotherapy at C University Hospital during a period of 5 days from March to May, 2003 were examined. Scores of INVR were measured once a day. Anxiety, anorexia, fatigue, and sleep satisfaction were measured before chemotherapy. Data was analyzed by repeated measures of ANOVA.

Results

The score of INVR increased over time during the days of hospitalization and showed a peak on the third day. The score was significantly higher on the third and consecutive cycles than on the first and second cycle. The score was significantly higher in patients in their forties and fifties rather than in their sixties. The score was higher in women than in men, and also increased as the sleep satisfaction decreased.

Conclusion

These results suggested that specific interventions for relief of nausea & vomiting were needed in middle age, women, the third chemotherapy cycle, and the third day after chemotherapy.

Citations

Citations to this article as recorded by

Analysis of Telephone Counseling of Patients in Chemotherapy Using Text Mining Technique
Seoyeon Kim, Jihyun Jung, Heiyoung Kang, Jeehye Bae, Kayoung Sim, Miyoung Yoo, Eunyoung, E. Suh
Asian Oncology Nursing.2022; 22(1): 46. CrossRef
The Relationships among Chemotherapy-Induced Nausea and Vomiting (CINV), Non-Pharmacological Coping Methods, and Nutritional Status in Patients with Gynecologic Cancer
Haerim Lee, Smi Choi-Kwon
Journal of Korean Academy of Nursing.2017; 47(6): 731. CrossRef
Influences of Stress, Anxiety, Depression, and Personality Trait on Nausea, Vomiting, and Retching of Breast Cancer Patients Receiving Chemotherapy
Yoo Wha Bhan, Hee-Yeon Choi, Woo Sung Lim, Byung-In Moon, Nam-Sun Paik, Weon-Jeong Lim
Journal of Korean Neuropsychiatric Association.2013; 52(5): 327. CrossRef
Effects of Progressive Muscle Relaxation on Nausea, Vomiting, Fatigue, Anxiety, and Depression in Cancer Patients Undergoing Chemotherapy
Young-Jae Kim, Nam-Sook Seo
Journal of Korean Oncology Nursing.2010; 10(2): 171. CrossRef
Transition of Symptoms and Quality of Life in Cancer Patients on Chemotherapy
Min Young Kim
Journal of Korean Academy of Nursing.2009; 39(3): 433. CrossRef

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Transition Model of Middle-aged Women: In Sook Cho, Young Sook Park; Journal of Korean Academy of Nursing 2004;34(3):515-524. Published online March 28, 2017; DOI: https://doi.org/10.4040/jkan.2004.34.3.515

Abstract PDF

Purpose

The purpose of this study was to develop and test a model to explain the transition state for Korean middle-aged women focusing on the transition concept.

Method

A hypothetical model was constructed based on the transition model of Schumacher & Meleis(1994) and tested. Thehypothetical model consisted of 5 latent variables and 11 observed variables. Exogenous variables were demographic characteristics, obstetric characteristics, and health behavior. Endogenous variables were transition state and quality of life with 6 paths. The data from 221 middle-aged women selected by convenience was analyzed using covariance structure analysis.

Result

The final model which was modified from the hypotheticalmodel improved to GFI=0.97, AGFI=0.94, NFI=0.94, and NNFI=0.95. The transition state was influenced directly by demographic characteristics, quality of life, and also indirectly by health behaviors. However, the influence of obstetric characteristics was not significant. The transition state was accountable for 68% of the variance by these factors.

Conclusion

These results suggest that enhancing health behaviors of the women are necessary to increase quality of life and it consequently contributes to improving the transition state. This model could be used to explain the health related vulnerability in these ages and to diagnosis individual women.

Citations

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Depression of married and employed women based on social-role theory
Insook Cho, Sukhee Ahn, Souk Young Kim, Young Sook Park, Hae Won Kim, Sun Ok Lee, Sook Hee Lee, Chae Weon Chung
Journal of Korean Academy of Nursing.2012; 42(4): 496. CrossRef
Development and Evaluation of a Transitional Care Program for Patients Discharged from Military Hospitals
Seun Young Joe
Journal of Korean Academy of Nursing.2010; 40(4): 599. CrossRef

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A Study on Regular Cervical Cancer Screening Behavior among Middle-aged Women: In Sook Cho, Young Sook Park; Journal of Korean Academy of Nursing 2004;34(1):141-149. Published online March 28, 2017; DOI: https://doi.org/10.4040/jkan.2004.34.1.141

Abstract PDF

Purpose

To identify the status of regular cervical cancer screening practices among middle-aged women, the associations between regular practice and research factors, and the predictive model and factors effecting such behavior was studied.

Method

Two hundred women, aged 40 to 60, were selected by convenience in one urban area of Seoul. They were asked about their regular attendance for screening, knowledge of cancer and screening, health belief, health self-determination index and certain personal factors.

Result

Approximately 54.5% of the women had periodic screening tests every 6 months to 2 years. Their knowledge of cervical cancer and health belief were at the medium level of each scale, but their health self-determination scores (HSDI) were low. Some influencing factors, and their cancer odds ratio were identified through univariate regression analysis. These variables were included in a predictive model, and this model proved to have enough fit and classification power (83.5%). In this model, the financial state, self-belief and self-determination scores were found to be significant.

Conclusion

Middle-age women's intrinsic motivation for healthy behavior was found to be low in those who felt to be in a poor financial state, had higher perceived barriers, lower perceived benefits and a lower prevalence of undergoing regular screening test.

Citations

Citations to this article as recorded by

Factors related to cancer screening behaviors
Boyoung Choi, Tae Rim Um, Kwang-Soo Lee
Epidemiology and Health.2018; 40: e2018011. CrossRef
Predictors of Follow-up Screening in Women with Abnormal Pap Smears
Young Suk Park, Jeong Sook Park
Asian Oncology Nursing.2014; 14(2): 84. CrossRef
Health Care Utilization in Women with Cervical Cancer and Cervical Intraepithelial Neoplasia
Hee Sun Kang, Hanju Lee
Asian Oncology Nursing.2013; 13(1): 37. CrossRef
Predictors Associated with Repeated Papanicolaou Smear for Cervical Cancer Screening
Eun-Joo Lee, Jeong-Sook Park
Asian Oncology Nursing.2013; 13(1): 28. CrossRef

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