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The applications of artificial intelligence (AI) in dementia research have garnered significant attention, prompting the planning of various research endeavors in current and future studies. The objective of this study is to provide a comprehensive overview of the research landscape regarding AI and dementia within scholarly publications and to suggest further studies for this emerging research field. A search was conducted in the Web of Science database to collect all relevant and highly cited articles on AI-related dementia research published in English until 16 May 2023. Utilizing bibliometric indicators, a search strategy was developed to assess the eligibility of titles, utilizing abstracts and full texts as necessary. The Bibliometrix tool, a statistical package in R, was used to produce and visualize networks depicting the co-occurrence of authors, research institutions, countries, citations, and keywords. We obtained a total of 1094 relevant articles published between 1997 and 2023. The number of annual publications demonstrated an increasing trend over the past 27 years. Journal of Alzheimer’s Disease (39/1094, 3.56%), Frontiers in Aging Neuroscience (38/1094, 3.47%), and Scientific Reports (26/1094, 2.37%) were the most common journals for this domain. The United States (283/1094, 25.86%), China (222/1094, 20.29%), India (150/1094, 13.71%), and England (96/1094, 8.77%) were the most productive countries of origin. In terms of institutions, Boston University, Columbia University, and the University of Granada demonstrated the highest productivity. As for author contributions, Gorriz JM, Ramirez J, and Salas-Gonzalez D were the most active researchers. While the initial period saw a relatively low number of articles focusing on AI applications for dementia, there has been a noticeable upsurge in research within this domain in recent years (2018–2023). The present analysis sheds light on the key contributors in terms of researchers, institutions, countries, and trending topics that have propelled the advancement of AI in dementia research. These findings collectively underscore that the integration of AI with conventional treatment approaches enhances the effectiveness of dementia diagnosis, prediction, classification, and monitoring of treatment progress.

Background: Several epidemiological studies have shown that psoriasis increases the risk of developing atrial fibrillation but evidence of this is still scarce. Aims: Our objective was to systematically review, synthesise and critique the epidemiological studies that provided information about the relationship between psoriasis and atrial fibrillation risk. Methods: We searched through PubMed, EMBASE and the bibliographies for articles published between 1 January 2000, and 1 November 2017, that reported on the association between psoriasis and atrial fibrillation. All abstracts, full-text articles and sources were reviewed with duplicate data excluded. Summary relative risks (RRs) with 95% CI were pooled using a random effects model. Results: We identified 252 articles, of these eight unique abstracts underwent full-text review. We finally selected six out of these eight studies comprising 11,187 atrial fibrillation patients. The overall pooled relative risk (RR) of atrial fibrillation was 1.39 (95% CI: 1.257–1.523, P < 0.0001) with significant heterogeneity (I2 = 80.316, Q = 45.723, τ2 = 0.017, P < 0.0001) for the random effects model. In subgroup analysis, the greater risk was found in studies from North America, RR 1.482 (95% CI: 1.119–1.964, P < 0.05), whereas a moderate risk was observed in studies from Europe RR 1.43 (95% CI: 1.269–1.628, P < 0.0001). Limitations: We were only able to include six studies with 11,178 atrial fibrillation patients, because only a few such studies have been published. Conclusion: Our results showed that psoriasis is significantly associated with an increased risk of developing atrial fibrillation. Therefore, physicians should monitor patient’s physical condition on a timely basis.

Levothyroxine is a widely prescribed medication for the treatment of an underactive thyroid. The relationship between levothyroxine use and cancer risk is largely underdetermined. To investigate the magnitude of the possible association between levothyroxine use and cancer risk, this retrospective case‐control study was conducted using Taiwan’s Health and Welfare Data Science Center database. Cases were defined as all patients who were aged ≥20 years and had a first‐time diagnosis for cancer at any site for the period between 2001 and 2011. Multivariable conditional logistic regression models were used to calculate an adjusted odds ratio (AOR) to reduce potential confounding factors. A total of 601 733 cases and 2 406 932 controls were included in the current study. Levothyroxine users showed a 50% higher risk of cancer at any site (AOR: 1.50, 95% CI: 1.46‐1.54; P, This retrospective case‐control study analyzed 23 million patients from Taiwan’s claims data. Levothyroxine use was significantly associated with an increased risk of brain, skin, pancreatic, and female breast cancers.

Previous epidemiological studies have raised the concern that the use of proton pump inhibitors (PPIs) is associated with an increased risk of kidney diseases. To date, no comprehensive meta-analysis has been conducted to assess the association between PPIs and the risk of chronic kidney disease (CKD). Therefore, we conducted a systematic review and meta-analysis to address the association between PPIs and CKD. The primary search was conducted in the most popular databases, such as PubMed, Scopus, and Web of Science. All observational studies evaluated the risk of CKD among PPI users, and non-users were considered for inclusion. Two reviewers conducted data extraction and assessed the risk of bias. Random-effect models were used to calculate pooled effect sizes. A total of 6,829,905 participants from 10 observational studies were included. Compared with non-PPI use, PPI use was significantly associated with an increased risk of CKD (RR 1.72, 95% CI: 1.02–2.87, p = 0.03). This updated meta-analysis showed that PPI was significantly associated with an increased risk of CKD. Association was observed in the same among moderate-quality studies. Until further randomized control trials (RCTs) and biological studies confirm these results, PPI therapy should not stop patients with gastroesophageal reflux disease (GERD). However, caution should be used when prescribing to patients with high-risk kidney disease.

BACKGROUND Research into mobile health (mHealth) technologies on weight loss, physical activity, and sedentary behavior has increased substantially over the last decade; however, no research has been published showing the research trend in this field. OBJECTIVE The purpose of this study was to provide a dynamic and longitudinal bibliometric analysis of recent trends of mHealth research for weight loss, physical activity, and sedentary behavior. METHODS A comprehensive search was conducted through Web of Science to retrieve all existing relevant documents published in English between January 1, 2010, and November 1, 2021. We developed appropriate research questions; based on the proven bibliometric approaches, a search strategy was formulated to screen the title for eligibility. Finally, we conducted bibliometric analyses to explore the growth rate of publications; publication patterns; and the most productive authors, institutions, and countries, and visualized the trends in the field using a keyword co-occurrence network. RESULTS The initial search identified 8739 articles, of which 1035 were included in the analyses. Our findings show an exponential growth trend in the number of annual publications of mHealth technology research in these fields. JMIR mHealth and uHealth (n=214, 20.67%), Journal of Medical Internet Research (n=71, 6.86%), and BMC Public Health (n=36, 3.47%) were the top 3 journals, publishing higher numbers of articles. The United States remained the leading contributor in these areas (n=405, 39.13%), followed by Australia (n=154, 14.87%) and England (n=125, 12.07%). Among the universities, the University of Sydney (n=36, 3.47%) contributed the most mHealth technology research in these areas; however, Deakin University (n=25, 2.41%) and the National University of Singapore (n=23, 2.22%) were in the second and third positions, respectively. CONCLUSIONS Although the number of papers published on mobile technologies for weight loss, physical activity, and sedentary behavior was initially low, there has been an overall increase in these areas in recent years. The findings of the study indicate that mobile apps and technologies have substantial potential to reduce weight, increase physical activity, and change sedentary behavior. Indeed, this study provides a useful overview of the publication trends and valuable guidance on future research directions and perspectives in this rapidly developing field.

Dina Nur Anggraini Ningrum,1– 3 Woon-Man Kung,4 I-Shiang Tzeng,4– 6 Sheng-Po Yuan,1,7 Chieh-Chen Wu,4 Chu-Ya Huang,8 Muhammad Solihuddin Muhtar,2 Phung-Anh Nguyen,2,9 Jack Yu-Chuan Li,1,2,10,11 Yao-Chin Wang12,13 1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; 2International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan; 3Public Health Department, Faculty of Sport Science, Universitas Negeri Semarang, Semarang City, Indonesia; 4Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei, Taiwan; 5Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan; 6Department of Statistics, National Taipei University, Taipei, Taiwan; 7Department of Otorhinolaryngology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan; 8Taiwan College of Healthcare Executives, Taipei, Taiwan; 9Department of Healthcare Information and Management, Ming Chuan University, Taoyuan, Taiwan; 10Department of Dermatology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; 11TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, Taiwan; 12Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan; 13Department of Emergency Medicine, Min-Sheng General Hospital, Taoyuan, TaiwanCorrespondence: Jack Yu-Chuan Li Email jack@tmu.edu.tw; jaak88@gmail.comYao-Chin Wang Email vkwang8888@yahoo.com.twPurpose: To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data.Patients and Methods: We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection.Results: This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (− 0.76% loss) and 0.9644 (− 0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features.Conclusion: Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program.Keywords: artificial intelligence, clinical decision support system, medical informatics application, precision medicine

Chest pain is a common complaint in the emergency department, but this may prevent a diagnosis of major adverse cardiac events, a composite of all-cause mortality associated with cardiovascular-related illnesses. To determine potential predictors of major adverse cardiac events in Taiwan, a pilot study was performed, involving the data from 268 patients with major adverse cardiac events, which was by an artificial neural network method. Nine biomarkers were selected for identifying non-ST-elevation myocardial infarction from common chest pain patients. By using a machine learning-based feature selection technique, five biomarkers were chosen from a set of 37 candidate variables. A full and a reduced risk stratification model were built. The full model was based on the characteristics of both invasive (i.e., creatinine and troponin I) and non-invasive (i.e., age, coronary artery disease risk factors, and corrected QT interval) variables, and the reduced model was based only on non-invasive variable characteristics. The full model showed a sensitivity of 0.948 and a specificity of 0.546 when the cutoff was set at 2 points, with a missed major adverse cardiac events rate of 1.32%, a positive predictive value of 0.228, and a negative predictive value of 0.987. High performance was also obtained with the five major biomarkers in the predictor built by the machine learning algorithm. The full model had the highest performance, but the reduced model can be applied as a quick and reasonably performing diagnostic tool.

Hospital admission rate for the patients with chest pain has already been increased worldwide but no existing risk score has been designed to stratify non-ST-elevation myocardial infarction (NSTEMI) from non-cardiogenic chest pain. Clinical diagnosis of chest pain in the emergency department is always highly subjective and variable. We, therefore, aimed to develop an artificial intelligence approach to predict stable NSTEMI that would give valuable insight to reduce misdiagnosis in the real clinical setting.A standard protocol was developed to collect data from chest pain patients who had visited the emergency department between December 2016 and February 2017. All the chest pain patients with aged20 years were primarily included in this study. However, STEMI, previous history of ACS, and out-of-hospital cardiac arrest were excluded from our study. An artificial neural network (ANN) model was then developed to predict NSTEMI patients. The accuracy, sensitivity, specificity, and receiver operating characteristic curve was used to measure the performance of this model.A total of 268 chest pain patients were included in this study; of those, 47 (17.5%) was stable NSTEMI, and 221 (82.5%) was unstable angina patients. Serval risk factors such as cardiac risk factor, systolic blood pressure, hemoglobin, corrected QT interval (QTc), PR interval, glutamic-oxaloacetic transaminase, glutamic pyruvic transaminase and troponin were independently associated with stable NSTEMI. The area under the receiver operating characteristic (AUROC) and accuracy of ANN were 98.4, and 92.86. Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value of the ANN model was 90.91, 93.33, 76.92, and 97.67 respectively.Our prediction model showed a higher accuracy to predict NSTEMI patients. This model has a potential application in disease detection, monitoring, and prognosis of chest pain at risk of AMI.

Background and objective Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for prevention, early diagnosis and treatment. We aimed to develop a machine learning model to predict FLD that could assist physicians in classifying high-risk patients and make a novel diagnosis, prevent and manage FLD. Methods We included all patients who had an initial fatty liver screening at the New Taipei City Hospital between 1st and 31st December 2009. Classification models such as random forest (RF), Naive Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) were developed to predict FLD. The area under the receiver operating characteristic curve (ROC) was used to evaluate performances among the four models. Results A total of 577 patients were included in this study; of those 377 patients had fatty liver. The area under the receiver operating characteristic (AUROC) of RF, NB, ANN, and LR with 10 fold-cross validation was 0.925, 0.888, 0.895, and 0.854 respectively. Additionally, The accuracy of RF, NB, ANN, and LR 87.48, 82.65, 81.85, and 76.96%. Conclusion In this study, we developed and compared the four classification models to predict fatty liver disease accurately. However, the random forest model showed higher performance than other classification models. Implementation of a random forest model in the clinical setting could help physicians to stratify fatty liver patients for primary prevention, surveillance, early treatment, and management.

Background: Cognitive impairment is one of the most common, burdensome, and costly disorders in the elderly worldwide. The magnitude of the association between anemia and overall cognitive impairment (OCI) has not been established. Objective: We aimed to update and expand previous evidence of the association between anemia and the risk of OCI. Methods: We conducted an updated systematic review and meta-analysis. We searched electronic databases, including EMBASE, PubMed, and Web of Science for published observational studies and clinical trials between 1 January 1990 and 1 June 2020. We excluded articles that were in the form of a review, letter to editors, short reports, and studies with less than 50 participants. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. We estimated summary risk ratios (RRs) with random effects. Results: A total of 20 studies, involving 6558 OCI patients were included. Anemia was significantly associated with an increased risk of OCI (adjusted RR (aRR) 1.39 (95% CI, 1.25–1.55, p &lt, 0.001)). In subgroup analysis, anemia was also associated with an increased risk of all-cause dementia (adjusted RR (aRR), 1.39 (95% CI, 1.23–1.56, 0.001)), Alzheimer’s disease [aRR, 1.59 (95% CI, 1.18–2.13, p = 0.002)], and mild cognitive impairment (aRR, 1.36 (95% CI, 1.04–1.78, p = 0.02)). Conclusion: This updated meta-analysis shows that patients with anemia appear to have a nearly 1.39-fold risk of developing OCI than those without anemia. The magnitude of this risk underscores the importance of improving anemia patients’ health outcomes, particularly in elderly patients.

Dina Nur Anggraini Ningrum,1,2 Sheng-Po Yuan,1,3 Woon-Man Kung,4 Chieh-Chen Wu,4 I-Shiang Tzeng,4– 6 Chu-Ya Huang,7 Jack Yu-Chuan Li,1,8,9,* Yao-Chin Wang10,11,* 1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; 2Public Health Department, Universitas Negeri Semarang, Semarang City, Indonesia; 3Department of Otorhinolaryngology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan; 4Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei, Taiwan; 5Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan; 6Department of Statistics, National Taipei University, Taipei, Taiwan; 7Taiwan College of Healthcare Executives, Taipei, Taiwan; 8Department Dermatology, Wan Fang Hospital, Taipei, Taiwan; 9Taipei Medical University Research Center of Cancer Translational Medicine, Taipei, Taiwan; 10Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan; 11Department of Emergency Medicine, Min-Sheng General Hospital, Taoyuan, Taiwan*These authors contributed equally to this workCorrespondence: Jack Yu-Chuan LiGraduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 15 F, No. 172-1, Sec 2, Kellung Road, Daan District, Taipei, 11031, TaiwanTel/Fax +886-2-2736-1661 Ext. 7600Email jack@tmu.edu.twYao-Chin WangDepartment of Emergency, Min-Sheng General Hospital, No. 168, ChingKuo Road, Taoyuan, 33044, TaiwanTel +886-3-317-9599 Ext. 8134Email vkwang8888@yahoo.com.twBackground: Incidence of skin cancer is one of the global burdens of malignancies that increase each year, with melanoma being the deadliest one. Imaging-based automated skin cancer detection still remains challenging owing to variability in the skin lesions and limited standard dataset availability. Recent research indicates the potential of deep convolutional neural networks (CNN) in predicting outcomes from simple as well as highly complicated images. However, its implementation requires high-class computational facility, that is not feasible in low resource and remote areas of health care. There is potential in combining image and patient’s metadata, but the study is still lacking.Objective: We want to develop malignant melanoma detection based on dermoscopic images and patient’s metadata using an artificial intelligence (AI) model that will work on low-resource devices.Methods: We used an open-access dermatology repository of International Skin Imaging Collaboration (ISIC) Archive dataset consist of 23,801 biopsy-proven dermoscopic images. We tested performance for binary classification malignant melanomas vs nonmalignant melanomas. From 1200 sample images, we split the data for training (72%), validation (18%), and testing (10%). We compared CNN with image data only (CNN model) vs CNN for image data combined with an artificial neural network (ANN) for patient’s metadata (CNN+ANN model).Results: The balanced accuracy for CNN+ANN model was higher (92.34%) than the CNN model (73.69%). Combination of the patient’s metadata using ANN prevents the overfitting that occurs in the CNN model using dermoscopic images only. This small size (24 MB) of this model made it possible to run on a medium class computer without the need of cloud computing, suitable for deployment on devices with limited resources.Conclusion: The CNN+ANN model can increase the accuracy of classification in malignant melanoma detection even with limited data and is promising for development as a screening device in remote and low resources health care.Keywords: skin cancer, convolutional neural network, artificial neural network, embedded artificial intelligence

Background: Recent epidemiological studies remain controversial regarding the association between statin use and reducing the risk of mortality among individuals with COVID-19. Objective: The objective of this study was to clarify the association between statin use and the risk of mortality among patients with COVID-19. Methods: We conducted a systematic articles search of online databases (PubMed, EMBASE, Scopus, and Web of Science) between 1 February 2020 and 20 February 2021, with no restriction on language. The following search terms were used: “Statins” and “COVID-19 mortality or COVID19 mortality or SARS-CoV-2 related mortality”. Two authors individually examined all articles and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for study inclusion and exclusion. The overall risk ratio (RRs) with 95% confidence interval (CI) was calculated to show the strength of the association and the heterogeneity among the studies was presented Q and I2 statistic. Results: Twenty-eight studies were assessed for eligibility and 22 studies met the inclusion criteria. Statin use was associated with a significantly decreased risk of mortality among patients with COVID-19 (RR adjusted = 0.64, 95% CI: 0.57–0.72, p &lt, 0.001). Moreover, statin use both before and after the admission was associated with lowering the risk of mortality among the COVID-19 patients (RR adjusted, before = 0.69, 95% CI: 0.56–0.84, p &lt, 0.001 and RR adjusted, after = 0.57, 95% CI: 0.54–0.60, p &lt, 0.001). Conclusion: This comprehensive study showed that statin use is associated with a decreased risk of mortality among individuals with COVID-19. A randomized control trial is needed to confirm and refute the association between them.

The potential impact of statins on the risk of Parkinson’s disease (PD) is still controversial; therefore, we conducted a comprehensive meta-analysis of observational studies to examine the effect of statin use on the risk of PD. We searched electronic databases, such as PubMed, EMBASE, Scopus, and Web of Science, for articles published between 1 January 2000 and 15 March 2022. Cohort studies which examined the association between statins and PD risk in the general population were also included. Two authors assessed the data and extracted all potential information for analysis. Random effects meta-analyses were performed to measure the risk ratio (RR) and 95% confidence intervals (CIs). Eighteen cohort studies including 3.7 million individuals with 31,153 PD participants were identified. In statin users, compared with non-users, the RR for PD was 0.79 (95% CI: 0.68–0.91). In a subgroup analysis of PD, this association was observed with medium and high quality, and the studies were adjusted for age, gender, and smoking status. When the data were stratified according to the duration of exposure, long-duration statin use was associated with a decreased risk of PD (RR = 0.49; 95% CI: 0.26–0.92). There was no significant decrease in the risk of PD in short-term statin users (RR = 0.94; 95% CI: 0.67–1.31). Moreover, no significant difference in the reduction in the risk of PD was observed between men (RR = 0.80; 95% CI: 0.75–0.86) and women (RR = 0.80; 95% CI: 0.75–0.86). Although our findings confirm a reduction in the PD risk associated with statin treatment and suggest that statins play a clinically favorable role, these findings should be interpreted with caution. Future randomized control trials with an ad hoc design are needed to confirm the potential utility of statins in reducing the risk of PD.

Background and Objective. People with anemia have higher rates of developing Parkinson disease (PD) than the general population. Previous epidemiological studies have invested the risk of PD in patients with anemia. However, the findings are still inconclusive. Therefore, we did a systematic review with meta-analysis to clarify the association between anemia and risk of PD. Methods. We systematically searched articles on electronic databases such as PubMed, Embase, Scopus, and Google Scholar between January 1, 2000 and July 30, 2020. Articles were independently evaluated by two authors. We included observational studies (case-control and cohort) and calculated the risk ratios (RRs) for associated with anemia and PD. Heterogeneity among the studies was assessed using the Q and I 2 statistic. We utilized the random-effect model to calculate the overall RR with 95% CI. Results. A total of 342 articles were identified in the initial searches, and 7 full-text articles were evaluated for eligibility. Three articles were further excluded for prespecified reasons including insufficient data and duplications, and 4 articles were included in our systematic review and meta-analysis. A random effect model meta-analysis of all 4 studies showed no increased risk of PD in patients with anemia ( N = 4 , R R adjusted = 1.17 (95% CI: 0.94-1.45, p = 0.15 ). However, heterogeneity among the studies was significant ( I 2 = 92.60 , p = < 0.0001 ). The pooled relative risk of PD in female patients with anemia was higher ( N = 3 , R R adjusted = 1.14 (95% CI: 0.83-1.57, p = 0.40 ) as compared to male patients with anemia ( N = 3 , R R adjusted = 1.09 (95% CI: 0.83-1.42, p = 0.51 ). Conclusion. This is the first meta-analysis that shows that anemia is associated with higher risk of PD when compared with patients without anemia. However, more studies are warranted to evaluate the risk of PD among patients with anemia.

Background: Sleep is recognized as an all-important physiological process, which also contributes to maintaining several bodily functions and systems. Insufficient sleep can induce many serious side effects and have an impact on human health in general. There are five consequences of lack of sleep, including an increased chance of morbidity and mortality, poor judgment, increased risk of accidents and injuries, reduced level of functioning, and an inferior quality of life. According to the Pittsburgh Sleep Quality Index (PSQI), also known as the most widely used tool in the field of subjective assessment of self-perceived sleep quality, a combination of acupoints could be more effective than single acupoint treatment in improving sleep quality. Methods: This study has been proved based on a previous meta-analysis that consisted of some relevant studies. The present study was based on the extracted randomized control trials (RCTs) rooted in a previous meta-analysis that worked on the basis of association rule mining and examined the potential kernel acupoint combinations for improving sleep quality. Results: Depending on the Apriori algorithm, we summarized 26 acupoints as binary data from the 32 eligible RCTs and analyzed them. The top 10 most frequently selected acupoints were HT7, SP6, PC6, KI1, GV20, Taiyang, Yintang, Anmian, KI3, and Ear Shenmen. Furthermore, as deduced from 21 association rules, the primary relevant rules in the combination of acupoints are {Yintang, Anmian}=>{GV20} and {HT7, KI1}=>{PC6}. Conclusions: In order to use acupuncture to improve sleep quality, integrating {Yintang, Anmian, GV20} with {HT7, KI1, PC6} acupoints could be deemed as the kernel acupoint combination.

Background: Psoriasis, a common chronic inflammatory disease, increases the risk of developing multiple sclerosis (MS), but evidence for this outcome is still unclear. However, we performed a meta-analysis of relevant studies to quantify the magnitude of the association between psoriasis and MS. It will help to assess the current state of knowledge, fill the gaps in our existing concern, and make a recommendation for future research. Methods: PubMed, EMBASE, and the bibliographies of articles were searched for studies published between January 1, 1990, and November 1, 2017, which reported on the association between psoriasis and MS. Articles were included if they (1) were published in English, (2) reported patients with psoriasis, and the outcome of interest was MS, (3) provided OR/RR/HR with 95% CI or sufficient information to calculate the 95% CI, and (4) if ≥50 patients. All abstracts, full-text articles, and sources were reviewed, with duplicate data excluded. Summary relative risk (ORs) with 95% CI was pooled using a random-effects model. Subgroup and sensitivity analyses were also conducted. Results: We selected 11 articles out of 785 unique abstracts for full-text review using our predetermined selection criteria, and 9 out of these 11 studies met all of our inclusion criteria. The overall pooled increased of developing MS in patients with psoriasis was RR 1.607 (95% CI 1.322–1.953, p < 0.0001) with low heterogeneity (I2 = 37.41%, Q = 12.782, τ2 = 0.027) for the random effect model. In the subgroup analysis, the MS risk in the patient with psoriasis was also significantly higher in the 6 studies from Europe RR 1.57 (95% CI 1.26–1.94, p < 0.001) with moderate heterogeneity (I2 = 50.66%, Q = 10.13, τ2 = 0.03) for the random effect model. Conclusion: Our results showed that psoriasis is significantly associated with an increased risk of developing MS. Physicians should carefully be observed symptoms and empower their patients to improve existing knowledge and quality of life. Further studies are warranted to establish the mechanisms underlying this relationship.

We performed a meta-analysis of relevant studies to quantify the magnitude of the association between proton pump inhibitors (PPIs) and risk of hip fracture. Patients with PPIs had a greater risk of hip fracture than those without PPI therapy (RR 1.20, 95% CI 1.14–1.28, p

Background The development of telecommunication has strongly affected comprehensive scientific disciplines, including medical sciences. Objective This study aims to assess the patient satisfaction of the teleconsultation system used for the consultation of dermatological follow-up care. Methods The study was performed cross sectional patient satisfaction survey method conducted between February and April 2017 to determine patient satisfaction using MedX teleconsultation system. The patient sample of the current study was recruited from cosmetic laser clinic, Wanfang and Taipei Medical University hospital. The study was performed on 32 patient (n = 32) participants. All of them were at least university graduate. Consultants and patients were handled using the Android-based MedX mobile application, which is available through an application for Google Android cellular telephones. Its application consists of a demographic information, structured step-by-step questionnaire, essential medical information about each patient, and digital images of skin lesions. Results 28 patients completed the questionnaire. The mean ± SD age of the patients was 27.25 ± 4.039 years; 78.6% were women. The study shows that respondents have reported a high level (85.8%) of mean overall satisfaction for the teleconsultation service. The usability of the system has highest satisfaction rate of 90.5% among the other subscales especially in terms of data transfer and data displayed. Responses of the patient satisfaction questionnaire were analyzed by age and gender, no statistically significant difference between the variables was found. Conclusion Patients have shown high satisfaction with teleconsultation service and it is well accepted in the management of cosmetic dermatology service. In the future, MedX application can be integrated into other instant messaging applications such as Line, thus allowing doctors and patients to easily communicate with each other.

To investigate whether the use of levothyroxine was associated with breast cancer risk. We conducted a population-based case–control study in Taiwan. Cases consisted of all patients who were aged 20 years and older, and had a first-time diagnosis of breast cancer for the period between 2001 and 2011. The controls were matched to the cases by age, sex, year, and month of diagnosis. Adjusted odd ratios (ORs) and 95% confidence intervals (CIs) were estimated by a conditional logistic regression. We examined 65,491 breast cancer cases and 261,964 controls. We found that use of levothyroxine was associated with a significant increase in breast cancer risk (OR 1.24, 95% CI 1.15–1.33; P

Background: A potential evidence from previous epidemiological studies remains conflicting findings regarding the association between atrial fibrillation (AF) and dementia risk. We, therefore, carried out a meta-analysis of relevant studies to investigate the magnitude of the association between AF and dementia risk. Methods: We performed a systematic literature search of PubMed, EMBASE, and Google Scholar for potential studies between January 1, 1990, and December 31, 2018, with no restriction on the publication language. All potential studies were independently assessed by two reviewers. We only included observational studies that calculated the odds ratio (OR)/hazards ratio (HR) for dementia associated with atrial fibrillation. We first assessed the heterogeneity among study-specific HRs using the Q statistic and I2 statistic. We then used the random-effects model to obtain the overall HR and its 95% CI for all studies. We also tested and corrected for publication bias by funnel plot–based methods. The quality of each study was assessed with the Newcastle Ottawa Scale. Results: A total of 16 studies with 2,415,356 individuals, and approximately 200,653 cases of incidence dementia were included in this study. Patients with AF had a greater risk of incidence dementia than those without AF (random-effect hazard ratio HR: 1.36, 95% CI: 1.23–1.51, p < 0.0001; I2 = 83.58). Funnel plot and Egger test did not reveal significant publication bias. However, limitations of the study included high heterogeneity and varying degrees of confounder adjustment across individual studies. Conclusion: This study serves as added evidence supporting the hypothesis that AF is associated with an increased risk of dementia. More studies are needed to establish whether optimal treatment of AF can reduce or mitigate the risk of dementia.

The demand for AI to improve patients outcome has been increased; we, therefore, aim to establish the diagnostic values of AI in diabetic retinopathy by pooling the published studies of deep learning on this subject. A total of eight studies included which evaluated deep learning in a total of 706,922 retinal images. The overall pooled area under receiver operating curve (AUROC) was 98.93% (95%CI:98.37%-99.49%). However, the overall pooled sensitivity and specificity for detecting referable diabetic retinopathy (RDR) was 74% (95% CI: 73%-74%), and 95% (95% CI: 95%-95%). The findings of this study show that deep learning had high sensitivity and specificity for identifying diabetic retinopathy.

Background and Aims: The impact of statin on dementia risk reduction has been a subject of debate over the last decade, but the evidence remains inconclusive. Therefore, we performed a meta-analysis of relevant observational studies to quantify the magnitude of the association between statin therapy and the risk of dementia. Methods: We systematically searched for relevant studies published from January 2000 to March 2018 using EMBASE, Google, Google Scholar, PubMed, Scopus, and Web of Science. Two authors performed study selection, data abstraction, and risk of bias assessment. We then extracted data from the selected studies and performed meta-analysis of observational studies using a random-effects model. Subgroup and sensitivity analyses were also conducted. Results: A total of 30 observational studies, including 9,162,509 participants (84,101 dementia patients), met the eligibility criteria. Patients with statin had a lower all-caused dementia risk than those without statin (risk ratio [RR] 0.83, 95% CI 0.79–0.87, I2 = 57.73%). The overall pooled reduction of Alzheimer disease in patients with statin use was RR 0.69 (95% CI 0.60–0.80, p < 0.0001), and the overall pooled RR of statin use and vascular dementia risk was RR 0.93 (95% CI 0.74–1.16, p = 0.54). Conclusion: This study suggests that the use of statin is significantly associated with a decreased risk of dementia. Future studies measuring such outcomes would provide useful information to patients, clinicians, and policymakers. Until further evidence is established, clinicians need to make sure that statin use should remain restricted to the treatment of cardiovascular disease.

Fatty liver disease (FLD) is considered the most prevalent form of chronic liver disease worldwide. The prediction of fatty liver disease is an important factor for effective treatment and reduce serious health consequences. We, therefore construct a prediction model based on machine learning algorithms. A dataset was developed with ten attributes that included 994 liver patients in which 533 patients were females and others were male. Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Logistic Regression (RF) data mining technique with 10-fold cross-validation was used in the proposed model for the prediction of fatty liver disease. The performances were evaluated with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. In this proposed model, logistic regression technique provides a better result (Accuracy 76.30%, sensitivity 74.10%, and specificity 64.90%) among all other techniques. This study demonstrates that machine learning models particularly logistic regression model provides a higher accurate prediction for fatty liver diseases based on medical data from electronic medical. This model can be used as a valuable tool for clinical decision making.

Addressed medication non-adherence, an important aspect in healthcare which significantly influences patients health and expenses to patients, healthcare providers and insurance companies.The PMMP platform implemented and tested through a randomized control trial to improve medication adherence, self-management of medication, and reducing long-term medication costs.The PMMP platform significantly reduced medication delays and missed medications and can therefore enhance patient health and overall disease and healthcare management. ObjectivesMedication non-adherence caused by forgetting and delays has serious health implications and causes substantial expenses to patients, healthcare providers, and insurance companies. We assessed the effectiveness of a personalized medication management platform (PMMP) for improving medication adherence, self-management medication, and reducing long-term medication costs. MethodsWe developed a mobile PMMP to reduce delayed and missed medications. A randomized control trial was conducted of three medical centers in Taiwan. A total 1198 participants who aged over 20 years, received outpatient prescription drugs for a maximum period of 14 days. 763 patients were randomly assigned to intervention group as receiving daily SMS reminders for their medications and 434 patients in control group did not. The primary outcome was change in delaying and forgetting medication between before and after intervention (after 7 days). ResultsMedication delays were reduced from 85% to 18% (67% improvement) after SMSs for the intervention group and from 80% to 43% (37% improvement) for the control group. Patients forgot medications were significantly reduced from 46% to 5% (41% improvement) for the experimental group after SMSs and from 44% to 17% (27% improvement) for the control group. The SMSs were considered helpful by 83% of patients and 74% of them thought SMSs help in controlling diseases. 92% of patients would recommend this system to their family and friends. ConclusionsA timely and personalized medication reminder through SMS can improve medication adherence in a nationalized healthcare system with overall savings in medication costs and significant improvements in health and disease management. Trial registrationClinicalTrials.gov: NCT02197689.

Electronic health record (EHR) is a digital data format that collects electronic health information about an individual patient or population. To enhance the meaningful use of EHRs, information extraction techniques have been developed to recognize clinical concepts mentioned in EHRs. Nevertheless, the clinical judgment of an EHR cannot be known solely based on the recognized concepts without considering its contextual information. In order to improve the readability and accessibility of EHRs, this work developed a section heading recognition system for clinical documents. In contrast to formulating the section heading recognition task as a sentence classification problem, this work proposed a token-based formulation with the conditional random field (CRF) model. A standard section heading recognition corpus was compiled by annotators with clinical experience to evaluate the performance and compare it with sentence classification and dictionary-based approaches. The results of the experiments showed that the proposed method achieved a satisfactoryF-score of 0.942, which outperformed the sentence-based approach and the best dictionary-based system by 0.087 and 0.096, respectively. One important advantage of our formulation over the sentence-based approach is that it presented an integrated solution without the need to develop additional heuristics rules for isolating the headings from the surrounding section contents.