Chi-Jie Lu
Biographic Data
| ID | 7788075 |
|---|---|
| NAME | Chi-Jie Lu |
| GIVEN NAMES | Chi-Jie |
| FAMILY NAME | Lu |
| SIGNATURE | LU C |
| AFFILIATIONS | Fu Jen Catholic University |
| ORCID | 0000-0002-7911-2253 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran
The new generation of nonvitamin K antagonists are broadly applied for stroke prevention due to their notable efficacy and safety. Our study aimed to develop a suggestive utilization of dabigatran through an integrated machine learning (ML) decision-tree model. Participants taking different doses of dabigatran in the Randomized Evaluation of Long-Term Anticoagulant Therapy trial were included in our analysis and defined as the 110 mg and 150 mg g…
An Integrated Machine Learning Scheme for Predicting Mammographic Anomalies in High-Risk Individuals Using Questionnaire-Based Predictors
This study aimed to investigate the important predictors related to predicting positive mammographic findings based on questionnaire-based demographic and obstetric/gynecological parameters using the proposed integrated machine learning (ML) scheme. The scheme combines the benefits of two well-known ML algorithms, namely, least absolute shrinkage and selection operator (Lasso) logistic regression and extreme gradient boosting (XGB), to provide ad…
Prevalence and Risk Factors of Metabolic Syndrome among the Homeless in Taipei City: A Cross-Sectional Study
The safety and health of homeless people are important social issues. Metabolic syndrome (MetS) is a sub-health-risk phenomenon that has been severely aggravated worldwide in recent years. The purpose of this study was to investigate the prevalence and risk factors of MetS among the homeless in Taipei City, Taiwan. In this study, a convenience sampling was conducted at homeless counseling agencies in Taipei City from April 2018 to September 2018.…
Associations of sleep duration with physical fitness performance and self-perception of health: A cross-sectional study of Taiwanese adults aged 23–45
Our research found that there were wide and different associations of sleep duration with physical fitness and self-perception of health among Taiwanese adults aged 23-45, and there were differences in these associated manifestations between men and women. This study could be of great importance in regional public health management in Taiwan, and provide inspirations for clinical research on physical fitness
The Effects of Daily Sleep Condition on Performances of Physical Fitness among Taiwanese Adults: A Cross-Sectional Study
Physical fitness is a powerful indicator of health. Sleep condition plays an essential role in maintaining quality of life and is an important marker that predicts physical fitness. This study aimed to determine the relationship between sleep conditions (sleep quality, sleep duration, bedtime) and multiple physical fitness indicators (body mass index (BMI), flexibility, abdominal muscle strength and endurance, cardiopulmonary endurance) in a well…
Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme
Influenza is a serious public health issue, as it can cause acute suffering and even death, social disruption, and economic loss. Effective forecasting of influenza outpatient visits is beneficial to anticipate and prevent medical resource shortages. This study uses regional data on influenza outpatient visits to propose a two-dimensional hierarchical decision tree scheme for forecasting influenza outpatient visits. The Taiwan weekly influenza ou…
Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals
Developing effective risk prediction models is a cost-effective approach to predicting complications of chronic kidney disease (CKD) and mortality rates; however, there is inadequate evidence to support screening for CKD. In this study, four data mining algorithms, including a classification and regression tree, a C4.5 decision tree, a linear discriminant analysis, and an extreme learning machine, are used to predict early CKD. The study includes…
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The Effects of Daily Sleep Condition on Performances of Physical Fitness among Taiwanese Adults: A Cross-Sectional Study
Physical fitness is a powerful indicator of health. Sleep condition plays an essential role in maintaining quality of life and is an important marker that predicts physical fitness. This study aimed to determine the relationship between sleep conditions (sleep quality, sleep duration, bedtime) and multiple physical fitness indicators (body mass index (BMI), flexibility, abdominal muscle strength and endurance, cardiopulmonary endurance) in a well…
Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme
Influenza is a serious public health issue, as it can cause acute suffering and even death, social disruption, and economic loss. Effective forecasting of influenza outpatient visits is beneficial to anticipate and prevent medical resource shortages. This study uses regional data on influenza outpatient visits to propose a two-dimensional hierarchical decision tree scheme for forecasting influenza outpatient visits. The Taiwan weekly influenza ou…
Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals
Developing effective risk prediction models is a cost-effective approach to predicting complications of chronic kidney disease (CKD) and mortality rates; however, there is inadequate evidence to support screening for CKD. In this study, four data mining algorithms, including a classification and regression tree, a C4.5 decision tree, a linear discriminant analysis, and an extreme learning machine, are used to predict early CKD. The study includes…
Prevalence and Risk Factors of Metabolic Syndrome among the Homeless in Taipei City: A Cross-Sectional Study
The safety and health of homeless people are important social issues. Metabolic syndrome (MetS) is a sub-health-risk phenomenon that has been severely aggravated worldwide in recent years. The purpose of this study was to investigate the prevalence and risk factors of MetS among the homeless in Taipei City, Taiwan. In this study, a convenience sampling was conducted at homeless counseling agencies in Taipei City from April 2018 to September 2018.…
Associations of sleep duration with physical fitness performance and self-perception of health: A cross-sectional study of Taiwanese adults aged 23–45
Our research found that there were wide and different associations of sleep duration with physical fitness and self-perception of health among Taiwanese adults aged 23-45, and there were differences in these associated manifestations between men and women. This study could be of great importance in regional public health management in Taiwan, and provide inspirations for clinical research on physical fitness
An Integrated Machine Learning Scheme for Predicting Mammographic Anomalies in High-Risk Individuals Using Questionnaire-Based Predictors
This study aimed to investigate the important predictors related to predicting positive mammographic findings based on questionnaire-based demographic and obstetric/gynecological parameters using the proposed integrated machine learning (ML) scheme. The scheme combines the benefits of two well-known ML algorithms, namely, least absolute shrinkage and selection operator (Lasso) logistic regression and extreme gradient boosting (XGB), to provide ad…
Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran
The new generation of nonvitamin K antagonists are broadly applied for stroke prevention due to their notable efficacy and safety. Our study aimed to develop a suggestive utilization of dabigatran through an integrated machine learning (ML) decision-tree model. Participants taking different doses of dabigatran in the Randomized Evaluation of Long-Term Anticoagulant Therapy trial were included in our analysis and defined as the 110 mg and 150 mg g…
Internal Medicine (7 works) · Medicine (7 works) · Computer Science (5 works) · Environmental health (4 works) · Cross-sectional study (3 works) · Decision tree (3 works) · Gerontology (3 works) · Logistic regression (3 works) · National Health and Nutrition Examination Survey (3 works) · Population (3 works)