Huan-Fang Lee

Dr. Huan-Fang Lee


Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 September 2018 in Journal of Nursing Management
Verified
Journal Article
Closed access
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HLHuan-Fang Lee
Huan-Fang Lee
HCHui-Ying Chiang
Hui-Ying Chiang
HKHui-Ting Kuo
Hui-Ting Kuo

1 August 2016 in Applied Nursing Research
Verified
Journal Article
Closed access
HLHuan-Fang Lee
Huan-Fang Lee
CKChia-Chi Kuo
Chia-Chi Kuo
TCTsair-Wei Chien
Tsair-Wei Chien
YWYu-Rung Wang
Yu-Rung Wang

18 March 2019 in Journal of Gastroenterology and Hepatology
Verified
Journal Article
Closed access
PDF file
CCChen‐Ju Chen
Chen‐Ju Chen
LWLiang‐Chao Wang
Liang‐Chao Wang
HKHsing‐Tao Kuo
Hsing‐Tao Kuo
YFYu‐Chiung Fang
Yu‐Chiung Fang
HLHuan-Fang Lee
Huan-Fang Lee

7 January 2019 in Journal of Nursing Management
Verified
Journal Article
Closed access
PDF file
HCHui‐Ying Chiang
Hui‐Ying Chiang
HLHuan-Fang Lee
Huan-Fang Lee
SLShu-Yuan Lin
Shu-Yuan Lin
SMShu‐Ching Ma
Shu‐Ching Ma

7 May 2020 in JMIR Medical Informatics
Verified
Journal Article
Open access
YLYi-Lien Lee
Yi-Lien Lee
WCWilly Chou
Willy Chou
TCTsair-Wei Chien
Tsair-Wei Chien
PCPo-Hsin Chou
Po-Hsin Chou
YYYu-Tsen Yeh
Yu-Tsen Yeh
HLHuan-Fang Lee
Huan-Fang Lee
Background: Burnout (BO), a critical syndrome particularly for nurses in health care settings, substantially affects their physical and psychological status, the institute’s well-being, and indirectly, patient outcomes. However, objectively classifying BO levels has not been defined and noticed in the literature. Objective: The aim of this study is to build a model using the convolutional neural network (CNN) to develop an app for automatic detection and classification of nurse BO using the Maslach Burnout Inventory–Human Services Survey (MBI-HSS) to help assess nurse BO at an earlier stage. Methods: We recruited 1002 nurses working in a medical center in Taiwan to complete the Chinese version of the 20-item MBI-HSS in August 2016. The k-mean and CNN were used as unsupervised and supervised learnings for dividing nurses into two classes (n=531 and n=471 of suspicious BO+ and BO−, respectively) and building a BO predictive model to estimate 38 parameters. Data were separated into training and testing sets in a proportion 70%:30%, and the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve) across studies for comparison. An app predicting respondent BO was developed involving the model’s 38 estimated parameters for a website assessment. Results: We observed that (1) the 20-item model yields a higher accuracy rate (0.95) with an area under the curve of 0.97 (95% CI 0.94-0.95) based on the 1002 cases, (2) the scheme named matching personal response to adapt for the correct classification in model drives the prior model’s predictive accuracy at 100%, (3) the 700-case training set with 0.96 accuracy predicts the 302-case testing set reaching an accuracy of 0.91, and (4) an available MBI-HSS app for nurses predicting BO was successfully developed and demonstrated in this study. Conclusions: The 20-item model with the 38 parameters estimated by using CNN for improving the accuracy of nurse BO has been particularly demonstrated in Excel (Microsoft Corp). An app developed for helping nurses to self-assess job BO at an early stage is required for application in the future.

29 March 2021 in Nutritional Neuroscience
Verified
Journal Article
Closed access
YCYu-Shiue Chen
Yu-Shiue Chen
HLHuan-Fang Lee
Huan-Fang Lee
CTChing-Hsuan Tsai
Ching-Hsuan Tsai
YHYu-Yun Hsu
Yu-Yun Hsu
CFChing-Ju Fang
Ching-Ju Fang
FHFang-Wen Hu
Fang-Wen Hu
Migraine is a common disease worldwide and migraine prevention is primarily currently based on pharmaceuticals. The mechanism of Vitamin B2 may positively contribute to migraine. This systematic re...