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(1) Background: Fibroblast growth factor 23 (FGF23) is predominantly secreted from bone and plays an important role in mineral balance in chronic kidney disease. However, the relationship between FGF23 and bone mineral density (BMD) in chronic hemodialysis (CHD) patients remains unclear. (2) Methods: This was a cross-sectional observational study that involved 43 stable outpatients on CHD. A linear regression model was used to determine risk factors for BMD. Measurements included serum hemoglobin, intact FGF23 (iFGF23), C-terminal FGF23 (cFGF23), sclerostin, Dickkopf-1, α-klotho, 1,25-hydroxyvitamin D, intact parathyroid hormone levels and dialysis profiles. (3) Results: Study participants had a mean age of 59.4 ± 12.3 years, and 65% were male. In the multivariable analysis, cFGF23 levels showed no significant associations with the BMD of the lumbar spine (p = 0.387) nor that of the femoral head (p = 0.430). However, iFGF23 levels showed a significant negative association with the BMD of the lumbar spine (p = 0.015) and that of the femoral neck (p = 0.037). (4) Conclusions: Among patients on CHD, higher serum iFGF23 levels, but not serum cFGF23 levels, were associated with lower BMD values of the lumbar spine and femoral neck. However, further research is required to validate our findings.

Background: Taiwan always had low case rates of COVID-19 compared with other countries due to its immediate control and preventive measures. However, the effects of its policies that started on 2020 for otolaryngology patients were unknown; therefore, the aim of this study was to analyze the nationwide database to know the impact of COVID-19 preventative measures on the diseases and cases of otolaryngology in 2020. Method: A case-compared, retrospective, cohort database study using the nationwide database was collected from 2018 to 2020. All of the information from outpatients and unexpected inpatients with diagnoses, odds ratios, and correlation matrix was analyzed. Results: The number of outpatients decreased in 2020 compared to in 2018 and 2019. Thyroid disease and lacrimal system disorder increased in 2020 compared to 2019. There was no difference in carcinoma in situ, malignant neoplasm, cranial nerve disease, trauma, fracture, and burn/corrosion/frostbite within three years. There was a highly positive correlation between upper and lower airway infections. Conclusions: COVID-19 preventative measures can change the numbers of otolaryngology cases and the distributions of the disease. Efficient redistribution of medical resources should be developed to ensure a more equitable response for the future.

Previous studies have indicated that HBV infection and T2DM are the factors that increase the risk of developing HCC. The experimental evidence has shown that antiglycemic agents may reduce the risk of HCC. However, the effect of dipeptidyl peptidase-4 inhibitors (DPP-4 inhibitors) on the risk of HCC in T2DM patients with chronic HBV infection remains unclear. In this retrospective cohort study, we extracted patients with T2DM and chronic HBV infection from the National Health Insurance Research Database (NHIRD) in Taiwan. The cases were divided into DPP-4 inhibitors use and non-use groups, according to whether they received DPP-4 inhibitors treatment, and the risk of HCC was compared between the two groups. At the end of the follow-up, approximately 2.33% of DPP-4 inhibitors users had received an HCC diagnosis compared with 3.33% of non-DPP-4 inhibitors users (p < 0.0001). After multivariate adjustment, DPP-4 inhibitors users showed a significant reduction in HCC risk (adjusted hazard ratios (aHRs): 0.53; 95% confidence intervals (CIs): 0.44–0.65). In conclusion, this population-based retrospective cohort study indicated that, in T2DM patients with chronic HBV infection, the use of DPP-4 inhibitors significantly reduced the risk of developing HCC compared with non-DPP-4 inhibitors use.

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 groups. The proposed scheme integrated ML methods, namely naive Bayes, random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost), which were used to identify the essential variables for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. RF (0.764 for 110 mg; 0.747 for 150 mg) and XGBoost (0.708 for 110 mg; 0.761 for 150 mg) had better area under the receiver operating characteristic curve (AUC) values than logistic regression (benchmark model; 0.683 for 110 mg; 0.739 for 150 mg). We then selected the top ten important variables as internal nodes of the CART decision tree. The two best CART models with ten important variables output tree-shaped rules for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. Our model can be used to provide more visualized and interpretable suggestive rules to clinicians managing NVAF patients who are taking dabigatran.

PurposeTo characterize the association between the frequency of screening for diabetic retinopathy (DR) and the detection of DR in patients with newly diagnosed type 2 diabetes mellitus (T2DM).MethodsThis nationwide population-based cohort study used data from the National Health Insurance Research Database to identify adult patients who were newly diagnosed with T2DM between 2000 and 2004. Data from their follow-up Diabetic retinopathy (DR) treatments over the next 10 years following diagnosis were also analyzed.ResultsThe 41,522 subjects were respectively assigned to a periodic screening group (n = 3850) and nonperiodic screening group (n = 37,672). Significant differences were observed between the two groups in terms of age, Charlson Comorbidity Index (CCI), sex, DR treatment, and the prevalence of DR. The association between periodic screening and DR treatment, only the elderly, female, and patient with severe CCI status showed the significance in the further stratified analysis.ConclusionPeriodic screening (annual or biannual screening in the first 5 years) was more effective than nonperiodic screening in detecting instances of DR in the middle-to-advanced aged group but not among younger patients. Screening pattern did not have a significant effect on the likelihood of DR-related treatment during the 5-year follow-up. It appears that a tight screening schedule for the first 5 years after diagnosis with diabetes is not necessary.

Lipid-lowering agents display limited benefits on cardiovascular diseases and mortality in patients undergoing dialysis. Therefore, they are not routinely recommended for dialysis patients. The aim of this study was to assess the effects of lipid-lowering agents on clinical outcomes in dialysis patients on the basis of real-world evidence. This research used Taiwan’s National Health Insurance Research Database to identify dialysis patients from January 2009 to December 2015; patients were then categorized into a case group treated with lipid-lowering agents (n = 3,933) and a control group without lipid-lowering agents (n = 24,267). Patients were matched by age, sex, and comorbidities in a 1:1 ratio. This study used the Cox regression model to estimate the hazard ratios (HRs) for mortality and major adverse cardiovascular events (MACEs) for events recorded until December 2017. During a mean follow-up period of approximately 3.1 years, 1726 [43.9%, incidence 0.123/person-year (PY)] deaths and 598 (15.2%, incidence 0.047/PY) MACEs occurred in the case group and 2031 (51.6%, incidence 0.153/PY) deaths and 649 (16.5% incidence 0.055/PY) MACEs occurred in the control group. In the multivariable analysis of the Cox regression model, lipid-lowering agent users showed a significantly lower risk of death [HR: 0.75; 95% confidence interval (CI): 0.70–0.80] and MACEs (HR: 0.88; 95% CI: 0.78–0.98) than lipid-lowering agent non-users. Moreover, the survival benefit of lipid-lowering agents was significant across most subgroups. Dialysis patients treated with lipid-lowering agents display a 25 and 12% reduction in their risk of mortality and MACEs, respectively. Therefore, lipid-lowering agents might be considered when treating dialysis patients with hyperlipidemia.

Background: Low-dose aspirin (100 mg) is widely used in preventing cardiovascular disease in chronic kidney disease (CKD) because its benefits outweighs the harm, however, its effect on clinical outcomes in patients with predialysis advanced CKD is still unclear. This study aimed to assess the effect of aspirin use on clinical outcomes in such group. Methods: Patients were selected from a nationwide diabetes database from January 2009 to June 2017, and divided into two groups, a case group with aspirin use (n = 3021) and a control group without aspirin use (n = 9063), by propensity score matching with a 1:3 ratio. The Cox regression model was used to estimate the hazard ratio (HR). Moreover, machine learning method feature selection was used to assess the importance of parameters in the clinical outcomes. Results: In a mean follow-up of 1.54 years, aspirin use was associated with higher risk for entering dialysis (HR, 1.15 [95%CI, 1.10–1.21]) and death before entering dialysis (1.46 [1.25–1.71]), which were also supported by feature selection. The renal effect of aspirin use was consistent across patient subgroups. Nonusers and aspirin users did not show a significant difference, except for gastrointestinal bleeding (1.05 [0.96–1.15]), intracranial hemorrhage events (1.23 [0.98–1.55]), or ischemic stroke (1.15 [0.98–1.55]). Conclusions: Patients with predialysis advanced CKD and anemia who received aspirin exhibited higher risk of entering dialysis and death before entering dialysis by 15% and 46%, respectively.

With improved survival in patients with cancer, the risk of developing multiple primary malignancies (MPMs) has increased. We aimed to characterize MPMs involving lung cancer and compare these characteristics between patients with single lung cancer and those with lung cancer and subsequent primary cancer (known as lung cancer first [LCF]). Methods: This retrospective study was conducted based on Taiwan Cancer Database from Taiwan’s National Health Insurance Registry Database. Patients with lung cancer (n = 72,219) from 1 January 2011 to 31 December 2015, were included in this study, and their medical records were traced back to 1 January 2002, and followed until 31 December 2019. Results: MPMs occurred in 10,577 (14.65%) patients with lung cancer, and LCF and other cancer first (OCF) accounted for 35.55% and 64.45% of these patients, with a mean age at lung cancer diagnosis of 65.18 and 68.92 years, respectively. The median interval between primary malignancies in the OCF group was significantly longer than that in the LCF group (3.26 vs. 0.11 years, p < 0.001). Patients in the single lung cancer group were significantly older than those in the LCF group (67.12 vs. 65.18 years, p < 0.001). The mean survival time of patients with LCF was longer than that of patients with single lung cancer. Following initial lung cancer, the three most common second primary malignancies were lung, colon, and breast cancers. For patients with advanced lung cancer, survival in patients with mutant epidermal growth factor receptor (EGFR) was longer than that in patients with undetected EGFR. In stage 3 and 4 patients with EGFR mutations, the LCF group showed better survival than the single lung cancer group. Conversely, in stage 1 patients with mutant EGFR, the LCF group exhibited worse survival than the single lung cancer group. Conclusions: Survival in patients with MPMs depends on baseline characteristics and treatments. Our findings may contribute to the development of precision medicine for improving personalized treatment and survival as well as the reduction of medical costs.

Issues related to artificial intelligence (AI) and ethics have gained much traction worldwide. The impact of AI on society has been extensively discussed. This study presents a bibliometric analysis of research results, citation relationships among researchers, and highly referenced journals on AI and ethics on a global scale. Papers published on AI and ethics were recovered from the Microsoft Academic Graph Collection data set, and the subject terms included “artificial intelligence” and “ethics.” With 66 nations’ researchers contributing to AI and ethics research, 1585 papers on AI and ethics were recovered, up to 5 July 2021. North America, Western Europe, and East Asia were the regions with the highest productivity. The top ten nations produced about 94.37% of the wide variety of papers. The United States accounted for 47.59% (286 articles) of all papers. Switzerland had the highest research production with a million-person ratio (1.39) when adjusted for populace size. It was followed by the Netherlands (1.26) and the United Kingdom (1.19). The most productive authors were found to be Khatib, O. (n = 10), Verner, I. (n = 9), Bekey, G. A. (n = 7), Gennert, M. A. (n = 7), and Chatila, R., (n = 7). Current research shows that research on artificial intelligence and ethics has evolved dramatically over the past 70 years. Moreover, the United States is more involved with AI and ethics research than developing or emerging countries.

Lung cancer survivors are at risk of developing second primary cancers (SPCs). Although some risk factors for the development of SPCs have been addressed, their impacts have not been clarified. This study, based on Taiwan’s National Health Insurance Research Database (NHIRD), a nationwide database, was designed to investigate the risk factors for SPCs in patients with initial lung cancer and identify the impacts of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) treatment on the development of SPCs. In this study, 37,954 individuals were included, of whom 2819 had SPCs. These patients were further divided into the second primary lung cancers (SPLC) and second primary extrapulmonary cancer (SPEC) groups. Among the patients with lung cancer without SPCs, those aged

Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals’ medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future.

Research has failed to resolve the dilemma experienced by localized prostate cancer patients who must choose between radical prostatectomy (RP) and external beam radiotherapy (RT). Because the Charlson Comorbidity Index (CCI) is a measurable factor that affects survival events, this research seeks to validate the potential of the CCI to improve the accuracy of various prediction models. Thus, we employed the Cox proportional hazard model and machine learning methods, including random forest (RF) and support vector machine (SVM), to model the data of medical records in the National Health Insurance Research Database (NHIRD). In total, 8581 individuals were enrolled, of whom 4879 had received RP and 3702 had received RT. Patients in the RT group were older and exhibited higher CCI scores and higher incidences of some CCI items. Moderate-to-severe liver disease, dementia, congestive heart failure, chronic pulmonary disease, and cerebrovascular disease all increase the risk of overall death in the Cox hazard model. The CCI-reinforced SVM and RF models are 85.18% and 81.76% accurate, respectively, whereas the SVM and RF models without the use of the CCI are relatively less accurate, at 75.81% and 74.83%, respectively. Therefore, CCI and some of its items are useful predictors of overall and prostate-cancer-specific survival and could constitute valuable features for machine-learning modeling.

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible.

Due to the impact of globalization, the competition between enterprises has become fierce and led the supply chains of many industries to be reorganized. One of the consequences is that the operation of many small and medium enterprises (SMs) had become very difficult. Hence, many of SMEs in Taiwan have gone bankrupt and some of them have moved to other places where they have lower production costs, in order to survive, this not only hollowed out the industries but also disconnected the supply chains in their mother countries. Because Taiwan’s SMEs are generally poor in innovation, this study explored the implementation of the theory of inventive problem-solving (TRIZ) with alignment of new product development (NPD) and supply chain management (SCM) to strengthen the innovation and productivity of new products, so that SMEs can refer to its use to aid sustainable business operation. We considered an SME in Taiwan as a case to study and investigate the strategies that it employed to achieve survival and sustainability. By examining the practical applications of the NPD of the case company, which was based on the TRIZ and NPD SCM alignment, we found that value-added products may be created despite unfavorable industry environments, by implementing and coordinating the TRIZ and three product-related variables, namely innovating, modularity, and variety. This study explored practical alternatives for SMEs to develop various value-added products that meet customers’ changing requirements and succeed in competitive markets to achieve a sustainable business operation. Considering SMEs are crucially important to the economic equality and development of countries and that SMEs may only survive for a short time when operating in changing supply chain environments, this study can be used as a reference for the management of SMEs and future academic research in related fields.

Background The relationship between sleep duration and physical fitness is one aspect of sleep health. Potential factors associated with sleep duration interfere with physical fitness performance, but the impact trends on physical fitness indicators remain unclear. Methods This study examined associations between sleep duration and physical fitness among young to middle-aged adults in Taiwan. A total of 42,781 Taiwanese adults aged 23–45 participated in the National Physical Fitness Examination Survey 2013 (NPFES-2013) in Taiwan between October 2013 and March 2014. A standardized structural questionnaire was used to record participants’ sleep duration, which was stratified as short ( Results By using analysis of covariance (ANCOVA), after sex grouping and age adjustment, we observed that sleep duration was significantly associated with obesity, functional fitness, and self-perception of health. The sleep duration for low obesity-related values (BMI, WHtR, and WHR) for men was 7–9 h/d, and that for women was 7–8 h/d. Sleeping more than 8 h/d showed poor functional fitness performances (BS and SR). For both sexes, sleep duration of 8–9 h/d was the optimal sleep duration for self-perceptions of health. Conclusions 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 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. A total of 297 homeless participants were recruited, from whom clinical indicators and questionnaire information were collected. Through statistical verification, analysis of variance (ANOVA), and logistic regression, we found the following main conclusions for homeless adults in Taipei: (1) The prevalence of MetS was estimated to be 53%, with 50% meeting four or more diagnostic conditions. (2) Dyslipidemia (high-density lipoprotein (HDL) deficiency and elevated triglyceride (TG)) showed the strongest association with the prevalence of MetS, more than 83% of people with HDL deficiency or hypertriglyceridemia had MetS. For the patient groups meeting more MetS diagnostic conditions, the values of high-density lipoprotein cholesterol (HDL-C), TG, and total cholesterol (TC) increased significantly. (3) The deterioration of MetS was significantly related to the high prevalence of hyperlipidemia (HL). (4) The homeless who were divorced, separated or widowed were more likely to suffer from MetS.

Introduction: In this study, pharmacists conducted home visits for individuals of medically underserved populations in Taiwan (i.e., socioeconomically disadvantaged individuals, middle-aged or older adults, and individuals living alone, with dementia, or with disabilities) to understand their medication habits. We quantified medication problems among various groups and investigated whether the pharmacist home visits helped to reduce the medication problems. Materials and Methods: From April 2016 to March 2019, pharmacists visited the homes of the aforementioned medically underserved individuals in Taipei to evaluate their drug-related problems and medication problems. Age, living alone, diagnoses of dementia or disabilities, and socioeconomic disadvantages contributed significantly to inadequate disease and medical treatment knowledge and self-care skills as well as lifestyle inappropriateness among patients. The patients who were living alone and socioeconomically disadvantaged stored their drugs in inappropriate environments. Results: After the pharmacists visited the patients’ homes twice, the patients improved considerably in their disease and medical treatment knowledge, self-care skills, and lifestyles (p < 0.001). Problems related to the uninstructed reduction or discontinuation of drug use (p < 0.05) and use of expired drugs (p < 0.001) were also mitigated substantially. Discussion and conclusion: Through the home visits, the pharmacists came to fully understand the medicine (including Chinese medicine) and health food usage behaviors of the patients and their lifestyles, enabling them to provide thorough health education. After the pharmacists’ home visits, the patients’ drug-related problems were mitigated, and their knowledge of diseases, drug compliance, and drug storage methods and environments improved, reducing drug waste. Our findings can help policymakers address the medication problems of various medically underserved groups, thereby improving the utilization of limited medical resources.

The effects of temperature and relative humidity on the growth of coronavirus disease 2019 (COVID-19) remain unclear. Data on the COVID-19 epidemic that were analyzed in this study were obtained from the official websites of the National Health Commission of China and the Health Commissions of 31 provinces in China. From January 26 to February 25, 2020, the cumulative number of confirmed COVID-19 cases in each region was counted daily using data from our database. Curve fitting of daily scatter plots of the relationship between epidemic growth rate (GR) with average temperature (AT) and average relative humidity (ARH) was conducted using the loess method. The heterogeneity across days and provinces was calculated to assess the necessity of using a longitudinal model. Fixed-effect models with polynomial terms were developed to quantify the relationship between variations in the GR and AT or ARH. An increased AT markedly reduced the GR when the AT was lower than -5°C, the GR was moderately reduced when the AT ranged from -5°C to 15°C, and the GR increased when the AT exceeded 15°C. ARH increased the GR when it was less than 72% and reduced the GR when it exceeded 72%. The temperature and relative humidity curves were not linearly associated with the GR of COVID-19. The GR was moderately reduced when the AT ranged from -5°C to 15°C. When the AT was lower or higher than -5°C to 15°C, the GR of COVID-19 increased. An increased ARH increased the GR when the ARH was lower than 72% and reduced the GR when the ARH exceeded 72%.

We evaluated the risk of dementia in patients with nasopharyngeal cancer (NPC) after undergoing radiation therapy (RT).Between January 1, 2000, and December 31, 2015, 594 patients newly diagnosed with NPC and treated with RT (NPC cohort) were identified from the Longitudinal Health Insurance Database (LHID) for this nationwide population-based matched cohort study. LHID is a subset of the National Health Insurance Research Database of Taiwan. We selected 2376 controls (non-NPC comparison cohort) using a four-fold propensity score-matched by sex, age, comorbidities, education level, tobacco abuse, and index date (the date when the patient received first RT). After adjusting for confounding factors, Fine and Gray's competing risk analysis compared dementia development between the NPC study cohort and non-NPC comparison cohort over the observation period from 2000 to 2015.Dementia development was 6.57% (39 of 594) and 4.42% (105 of 2376) in the NPC study cohort and non-NPC comparison cohort, respectively. Patients with NPC receiving RT were more likely to develop dementia than the comparison cohort, with a crude hazard ratio (HR) of 1.63 [95% confidence interval (CI) = 1.25-2.13, P 0.001]. After adjusting for age, sex, education level, tobacco abuse, comorbidity, geographic area, urbanization level of the residence, and care level, the adjusted HR was 1.91 (95% CI = 1.42-2.51, P 0.001).Patients with NPC receiving RT had a 1.91-fold higher risk of dementia than the non-NPC comparison controls.

Background: Although a shared decision-making (SDM) process integrates patient values and evidence-based medicine, patients’ anxiety and decision conflicts remain. Thus, we propose a new decision-making model integrating a machine-learning algorithm to investigate its feasibility for reducing anxiety, decision conflicts, and increasing satisfaction after making a decision.Methods: We enrolled participants willing to undergo the SDM process for a prostate-specific antigen (PSA) blood test and obtained data including age, PSA knowledge, if they have a friend with prostate cancer, perceptive risk of prostate cancer, International Prostate Symptom Score and Importance for Physiological and Psychological Impact in PSA Testing scores, personal values, and their final decisions, including “Accept” PSA blood test or “Not now,” to build the dataset for training the following machine-learning models: multilayer perceptron neural network, random forest (RF), extreme gradient boosting, support vector machine, and deep learning neural network. Uniform parameter tuning and model comparison were implemented. The best model was used for a randomized controlled trial (RCT), in which we measured the effects of personalized suggestions generated by the machine-learning model on anxiety, decision satisfaction, and decision conflicts.Results: RF was the best algorithm for building models with our dataset from 507 subjects (mean AUC: 0.8801, mean ACC: 0.8313, Max ACC: 0.8933). Therefore, we used the RF model for RCT with 185 and 182 subjects in the machine-learning suggestion group (MLSG) and control group (CG), respectively. The MLSG patients were calmer, more content, and less worrisome than those in the CG. They also experienced higher decision satisfaction and less decision conflict, including more decision support, advice, assurance of decision, ease of decision-making, and adherence to decision. Moreover, participants who were suggested “Accept” by the model were more likely to make “Accept” their final decision than the CG participants (50.75% vs 24.18%, χ2 = 16.07, p < 0.000). The “Not now” suggestion followed a similar trend.Conclusions: A highly accurate machine-learning model was constructed using our methods. Personalized suggestions generated from this model yielded increased satisfaction and reduced anxiety and decision conflict. Patients tended to take machine-learning suggestions as their final decision.Trial name: Shared Decision Making: Decision Tree and Artificial Neural Network Assisted Decision Aid for PSA ScreeningTrial registration: ChiCTR, ChiCTR2000034126. Registered 25 June 2020 – Retrospectively registered, http://www.chictr.org.cn/ChiCTR2000034126

Background: The effects of temperature and humidity on the epidemic growth of coronavirus disease 2019 (COVID-19)remains unclear.Methods: Daily scatter plots between the epidemic growth rate (GR) and average temperature (AT) or average relative humidity (ARH) were presented with curve fitting through the “loess” method. The heterogeneity across days and provinces were calculated to assess the necessity of using a longitudinal model. Fixed effect models with polynomial terms were developed to quantify the relationship between variations in the GR and AT or ARH.Results: An increased AT dramatically reduced the GR when the AT was lower than −5°C, the GR was moderately reduced when the AT ranged from −5°C to 15°C, and the GR increased when the AT exceeded 15°C. An increasedARH increased theGR when the ARH was lower than 72% and reduced theGR when the ARH exceeded 72%.Conclusions: High temperatures and low humidity may reduce the GR of the COVID-19 epidemic. The temperature and humidity curves were not linearly associated with the COVID-19 GR.

Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6&ndash, 9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments&rsquo, health departments to locate potential and high-risk outbreak areas.

Purpose: Predicting the number of novel Coronavirus Disease 2019 (COVID-19) new-suspected or confirmed cases is crucial in the prevention and control of the COVID-19 outbreak. Methods: Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from December 31, 2019, to February 9, 2020. The new-suspected cases of COVID-19 data were collected from January 20, 2020, to February 9, 2020. We used the lagged series of SMSI to predict new-suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new-suspected COVID-19 case numbers over the subsequent 20 days. We further validated the optimal method for new-confirmed cases of COVID-19 from December 31, 2019, to February 17, 2020. Results: The new-suspected COVID-19 case numbers were significantly correlated with the lagged series of SMSI. SIMI could be earlier detected 6–9 days than new-suspected cases of COVID-19. The optimal method was subset select, which had the lowest estimation error and a moderate number of predictors. The subset selection method was also significantly correlated with the new-confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new-confirmed COVID-19 cases. Conclusions: SMSI could be a significant predictor of the number of COVID-19 infections. Funding Statement: Lo-Hsu Medical Foundation, Lotung Poh-Ai Hospital, supports SzuYuan Wu’s work (Funding Number: 10908 and 10909). Lei Qin's work is supported by University of International Business and Economics Huiyuan outstanding young scholars research funding (17YQ15), "the Fundamental Research Funds for the central Universities" in UIBE (CXTD10-10). Ben-Chang Shia’s work was also supported by an institutional grant from Taipei Medical University (Taipei, Taiwan) for New Faculty Research (TMU103-AE1-B22). Declaration of Interests: The authors have no potential conflicts of interest to declare.

Background: The incidence of female BC among the Eastern and Southeastern Asian populations has gradually increased in recent years. However, epidemiological studies on the relationship between a sedentary lifestyle and female BC are insufficient. In order to determine the association between this lifestyle and the incidence of female BC, we conducted a population-based cohort study on women in Taiwan. Methods: We followed a prospective cohort of 5879 women aged 30 years and over enrolled in the 2001 National Health Interview Survey (NHIS), who developed female BC over a period of 72,453 person years, and we estimated the hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) using the Cox proportional hazards model. Results: RFs associated with female BC incidence included parity (adjusted HR = 0.63; 95% CI: 0.44–0.91), body mass index (adjusted HR = 1.34; 95% CI: 1.04–1.71), and ≥3 h/day spent sitting (adjusted HR = 1.89; 95% CI: 1.08–3.32). The incidence of female BC in participants who sat for ≥3 h/day and consumed sugary drinks was 2.5 times greater than that in those who sat for

This paper tries to trace our research history briefly from Barlow and Proschan to attain general replacement models. We begin with a random age replacement policy that is planned at a random time Y and call it as random replacement. When the distribution of Y becomes a degenerate distribution placing unit mass at T, age replacement is formulated. We obtain the general formulas for optimum replacement times. We next suppose the unit works for a job with random works, and replacement policies with N cycles are discussed. As follows, we combine age and random replacement models and discuss replacement first, replacement last, replacement overtime, replacement overtime first and replacement overtime last. By formulating the distributions of replacement times with n variables, general replacement models with n replacement times are obtained.

Background: The decision to sign a do-not-resuscitate (DNR) consent is critical for patients concerned about their end-of-life medical care. Taiwan’s National Health Insurance Administration (NHIA) introduced a family palliative care consultation fee to encourage family palliative care consultations; since its implementation, identifying which families require such consultations has become more important. In this study, the Taiwanese version of the Palliative Care Screening Tool (TW–PCST) was used to determine each patient’s degree of need for a family palliative care consultation. Objective: This study analyzed factors associated with signing DNR consents. The results may inform family palliative care consultations for families in need, thereby achieving a higher DNR consent rate and promoting the effective use of medical resources, including time, labor, and funding. Method: In this retrospective study, logistic regression analysis was conducted to determine which factors affected the DNR decisions of 2144 deceased patients (aged ≥ 20 years), whose records were collected from the Taipei City Hospital health information system from 1 January to 31 December 2018. Results: Among the 1730 patients with a DNR consent, 1298 (75.03%) received family palliative care consultations. The correlation between DNR consent and family palliative care consultations was statistically significant (p < 0.001). Through logistic regression analysis, we determined that participation in family palliative care consultation, TW–PCST score, type of ward, and length of stay were significant variables associated with DNR consent. Conclusions: This study determined that TW–PCST scores can be used as a measurement standard for the early identification of patients requiring family palliative care consultations. Family palliative care consultations provide opportunities for patients’ family members to participate in discussions about end-of-life care and DNR consent and provide patients and their families with accurate medical information regarding the end-of-life care decision-making process. The present results can serve as a reference to increase the proportion of patients willing to sign DNR consents and reduce the provision of ineffective life-prolonging medical treatment.

Accumulating evidence has shown a significant correlation between periodontal diseases and systemic diseases. In this study, we investigated the association between the frequency of tooth scaling and acute myocardial infarction (AMI). Here, a group of 7164 participants who underwent tooth scaling was compared with another group of 7164 participants without tooth scaling through propensity score matching to assess AMI risk by Cox’s proportional hazard regression. The results show that the hazard ratio of AMI from the tooth scaling group was 0.543 (0.441, 0.670) and the average expenses of AMI in the follow up period was USD 265.76, while the average expenses of AMI in follow up period for control group was USD 292.47. The tooth scaling group was further divided into two subgroups, namely A and B, to check the influence of tooth scaling frequency on AMI risk. We observed that (1) the incidence rate of AMI in the group without any tooth scaling was 3.5%, which is significantly higher than the incidence of 1.9% in the group with tooth scaling, (2) the tooth scaling group had lower total medical expenditures than those of the other group because of the high medical expenditure associated with AMI, and (3) participants who underwent tooth scaling had a lower AMI risk than those who never underwent tooth scaling had. Therefore, the results of this study demonstrate the importance of preventive medicine.

It would be of interest to formulate the general replacement models, combing the constant and random policies to satisfy the commonly planned and randomly needed replacement times. This paper takes up age and periodic replacement models again to formulate their general models when replacement actions are also conducted at random times $$Y_i~(i=1,2,\ldots ,n)$$ . The classic approach of whichever occurs first and the newly proposed approach of whichever occurs last are used for such general models, whose models are named as replacement first, modified replacement first, replacement last and modified replacement last, respectively. We compare all of the replacement models analytically and numerically to find which policy should be selected from the viewpoint of cost. It is shown that the modified replacement policies with combined approaches of whichever occurs first and last are more economical than others. In addition, the replacement models with different replacement costs are extended for further studies.

This research develops a new performance indicator for material flow effectiveness in production systems. The so-called flow value can represent the smoothness of material flow in a production system, thus can be used as real time indicator to instantly reveal the performance of a production system. A simulation study on a machining equipment manufacturing company in Taiwan validates the usefulness of using flow value as an indicator of material flow performance. This flow value is also capable of revealing areas where improvement can be made to effectively improve total system's performance in terms of material flow.

We studied the effects of physical fitness performance on happiness in Taiwanese adults. Through the MARS model, we analyzed the impact factors and found that age, smoking, sleep duration, performance of athletic ability and sedentary lifestyle significantly affected happiness

Prostate cancer is a common cancer treated with multi-modality. The combinations of modalities are numerous and complex. Clinical practice guidelines and rules have already been proven in many studies. However, the hypotheses of these studies came from physicians' and experts' experiences and observation. Association analysis, as an importance component of data mining, has been proved to be helpful for us to discover rules from big medical databases. We believe association analysis is able to help us to discover new rules between comorbidities and modalities in subjects of prostate cancer, so that employed it to analyze prostate cancer dataset derived from million people file of NHIRD. We successfully found six rules and rule 1,2,3,5,6 could be well explained with known knowledge and literatures, which were "Young prostate cancer patient who were spared from definite treatment tend to be spared from HT.", "TRUS is associated with younger age group, while TURP is associated with older Age.", "RT is associated with HT.", "CT is highly associated with RT.", "Hemiplegia, cerebrovascular disease, moderate to severe renal disease, diabetes with end organ damage is associated with TURP. Patients with TURP are associated with more comorbidity." We also discovered rule 4: "Younger patients who received HT is highly associated with previous RP.", which are still hypothesis and deserve our validation.

The global innovation of information technology fundamentally reshaped the finance technology and the flourishing development over the past decade. The high coverage of both the internet network and the mobile phone has facilitated Taiwan to become one of the most potential internet finance regions. This study aims to empirically validate the technology acceptance model (TAM 3) for exploring the key factors that influence the internet finance development. SEM was adopted to investigate both the hypothesis and the path coefficient associations among the constructs and their significance. Confirmed factor analysis for the instrument validity and reliability assessment was made. Hierarchy regression was performed to test the moderator effect of experience and voluntariness. This study result found that Taiwan finance customers had high expectations of information security and preferred high technology products with complex functions. The result demonstrated that customer’s Use Behavior was influenced by Perceived of Usefulness, not Perceived of Ease of Use. The moderator effect result in Experience had a positive moderator effect on Objective Usability to Perceived Ease of Use. This research extracted the key factors that influenced the internet finance development in Taiwan for providing both local government and internet finance developing countries guidelines while concurrently developing an internet finance development strategy.

This study examines the relationship between social media site Facebook and TV ratings drawing from audience factors of integration model of audience behavior. Based on context of Taiwan television network programs, this study collected measures for Facebook likes, shares, comments, posts for three genres of television shows and their Nielsen ratings over a period of eleven weeks, resulting in the size of sample more than 130 observations. This study applied multiple regression models and determined that the key social media measures correlate with TV ratings. In essence, TV shows with higher number of posts and engagement are likely to relate to higher ratings, special in drama shows. Subsequently, this study constructed the TV prediction models with measures for Facebook via SVR. The results suggested that prediction models are a good forecasting of which MAPE was between 10% - 20%, even less than 10%. This implies that TV network should be motivated to invest in social media and engage their audience and analysts can use social media as a mechanism of exante forecasting.

The purpose of this study is to understand the relationship among leisure physical activity, sedentary lifestyle, physical fitness, and happiness in healthy elderly adults aged over 65 years old in Taiwan. Data were recruited from the National Physical Fitness Survey in Taiwan, which was proposed in the Project on the Establishment of Physical Fitness Testing Stations by the Sports Administration of the Ministry of Education. Participants were recruited from fitness testing stations set up in 22 counties and cities from October 2015 to May 2016. A total of 20,111 healthy older adults aged 65&ndash, 102 years were recruited as research participants. The fitness testing procedure was described to all participants, who were provided with a standardized structured questionnaire. Participants&rsquo, data included sex, city or county of residence, living status (living together with others or living alone), education level, and income. Physical fitness testing was conducted in accordance with The Fitness Guide for Older Adults published by the Sports Administration of the Ministry of Education. The testing involved cardiorespiratory endurance, muscle strength, muscle endurance, flexibility, balance, and body composition. The t-test was used to evaluate the differences between continuous and grade variables under the two classification variables of sex, city or county of residence, and living status. We used the MARS (multivariate adaptive regression splines) model to analyze the effects of physical fitness variables and leisure physical activity variables on happiness. Among healthy elderly adults, sex, age, living status, body mass index, and leisure physical activity habits proved to be related to happiness. Aerobic endurance (2-min step test), muscular strength and endurance (30-s arm curl and 30-s chair stand tests), flexibility (back stretch and chair sit-and-reach tests), and balance ability (8-foot up-and-go tests and one-leg stance with eyes open tests) were found to be related to happiness. The results of this study indicate that increased physical activity and intensity, as well as physical fitness performance, are associated with improved happiness.

The number of people using internet has increased due to advances in information technology, and the growth of electronic commerce (e-commerce) is increasingly growing. This study aims to investigate the factors that influence consumer intention to adopt e-commerce in Jakarta and Taipei. In this paper, additional variables such as trust, costs, social influences, various services, and control variables such as age, educational level, and gender of consumers become work extension of traditional model of technology acceptance (TAM) and the diffusion of innovation (DOI) models. By comparing consumers from both Jakarta and Taipei, this research is able to form a comparison model based on two different cultural settings. Data are collected from 192 Jakarta consumers and 184 Taipei consumers, and ANCOVA is employed to test the research model. The results show that trust, cost, social in?uence, and variety of services, perceived usefulness and trialability have significant influence to Jakarta consumer decisions to adopt e-commerce. All of variables except variety of services have significant influence to Taipei consumer decisions to adopt e-commerce. Demographic profile such as age, gender and education level have no significant influence to both cities consumers. Results from this study will be useful for online companies in formulating marketing strategy, especially for companies that will operate in Indonesian market, since Indonesia is a huge and growing market.

In this paper, we composite the three kinds of performances including the general, the must-be and the attractive demands as a total performance of each attribute and estimating the importance degree of each attribute by weighting of regression. Furthermore, we propose an improved validity index to compare the total accomplishments of the different decided IPAs or kano models, this new index is a three-way method including not only which quadrant of each attribute is belong to and how closed the distance between the attribute and the secondary diagonal line, but also how closed the distance between the attribute and the origin of the coordinates, it is more sensitive and useful than the Liu's 2-ways validity index.

Although a previous study found that neural network forecasts were more accurate than time series models for predicting Latin American stock indexes, the forecasting accuracy of neural network for predicting gold futures prices has never been discussed. Therefore, the first objective of this study is to compare the forecasting accuracy of a neural network model with that of ARIMA models. Furthermore, the fluctuations in gold futures are not only influenced by the quantitative variables, but also by many nonquantifiable factors, such as wars, international relations, and terrorist attacks. The second objective of this study is therefore to propose the integration of text mining and an artificial neural network to forecast gold futures prices. The historical gold futures prices from 1999 to 2008 were used as training data and testing data, and the prices of 2009 were used to examine the effectiveness of the proposed model. The results of empirical analysis showed that an artificial neural network forecasted...

HeA new machine will not fail easily in the early stage of its useful life. This phenomenon is consistent with the fact that the optimal sequential preventive maintenance (PM) policy which has longer PM intervals in its earlier stage of lifetime. In this paper, we propose a failure-rate-reduction periodic PM model with delayed initial time in a finite time span. Then, the optimal periodic PM policy is developed by minimizing the expected total maintenance cost, which can have smaller expected total maintenance cost than the optimal policy of the original failure-rate-reduction periodic PM model. The algorithm of finding the optimal PM policy for the proposed PM model is developed. Finally, examples are illustrated to verify the optimal policies of the proposed new PM model.

It has been proposed in recent literatures that if replacement time is planned too early prior to failure time, a waste of operation cost, i.e., excess costs, would incur because the system might run for an additional period of time to complete critical operations; and if the replacement time is too late after failure, a great failure cost, i.e., shortage cost, is incurred due to the delay in time of the carelessly scheduled replacement. The above notion of shortage and excess costs are taken into consideration for the replacement first, replacement last and replacement overtime models in this paper. We obtain the expected cost rates and their optimum replacement times. Comparisons among these optimum times are made analytically and numerically.

This paper studies the optimal redundant policies for a manufacturing system processing jobs with random working times. The redundant units of the parallel systems and standby systems are subject to stochastic failures during the continuous production process. First, a job consisting of only one work is considered for both redundant systems and the expected cost functions are obtained. Next, each redundant system with a random number of units is assumed for a single work. The expected cost functions and the optimal expected numbers of units are derived for redundant systems. Subsequently, the production processes of N tandem works are introduced for parallel and standby systems, and the expected cost functions are also summarized. Finally, the number of works is estimated by a Poisson distribution for the parallel and standby systems. Numerical examples are given to demonstrate the optimization problems of redundant systems.

This paper compares the standby and parallel systems through the following analyses: reliability measures such as reliabilities, mean times to failure, and failure rates are first compared. These comparisons are reconsidered when the number of units of the two systems cannot be predefined constantly but is a random variable that can be estimated. Replacement policies in which how many number of units should be provided for replacements are compared second. When the two systems work for the same job with a random processing time, their optimal scheduling problems are compared third by employing excess and shortage costs. Finally, the standby and parallel modes are applied into data transmission system, and their mean transmission times are compared. All comparative discussions are given analytically and numerically in exponential distributions, and their potential values could be explored in future studies. This work is partially supported by the Qatar National Research Fund (grant number NPRP 4-631-2-233) and the National Science Council of Taiwan (grant number NSC 102-2221-E-030-018). Scopus

It has been assumed in most maintenance models that (i) the number of units for a parallel system can be predefined precisely, (ii) maintenance cost after failure should be avoided, and (iii) age replacement can always be performed at its optimized times. However, these assumptions are challenged from practical perspectives in this paper. For this purpose, shortage and excess costs, which claim that replacement done too early before failure involves a waste of operation, are introduced into replacement models, such as replacement plans with time T or distribution G(t) for constant n or random N of units. Furthermore, the number N of working times and the number n of available units for a scheduled replacement time T are discussed. For each model, the expected replacement cost for one cycle and expected replacement cost rate for a long run are optimized, and numerical examples are given. 2016 Elsevier Ltd. All rights reserved. This work is partially supported by Postdoctoral Research Award PDRA1-0116-14107 from the Qatar National Research Fund, and Ministry of Science and Technology of Taiwan MOST 103-2221-E-030 -008 . Scopus