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Background: Schizophrenia is one of the leading causes of disability. Paliperidone palmitate once-monthly injection (PP1M) was developed to provide consistent drug delivery and improve medication adherence for maintenance treatment. It is well known that patients with schizophrenia have higher cardiovascular risks, however little is known about the cardiovascular risks of patients with schizophrenia treated with PP1M in Asia. Objective: This study aimed to estimate the incidence of cardiovascular events after initiating PP1M treatment and evaluate the cardiovascular risk associations compared with oral second-generation antipsychotics (SGAs). Methods: Data from Taiwan's National Health Insurance Research Database were used to identify a cohort of adult patients with schizophrenia who received any SGAs from 1 March 2012 to 31 December 2018. Patients who initiated PP1M treatment were enrolled for descriptive analysis of incidence rates. PP1M patients were propensity matched 1:1 to patients initiating a new oral SGA, for comparative analysis based on demographics, clinical characteristics and treatment history at baseline, in three-step matching procedures, following the prevalent new-user design to enhance comparability. Follow-up ended at the end of the treatment episode of index drug, death, last record available, or end of the study (31 December 2019). Study endpoints included serious cardiovascular events (including severe ventricular arrhythmia and sudden death), expanded serious cardiovascular events (which further included acute myocardial infarction and ischemic stroke), and cardiovascular hospitalizations. Risks of study endpoints between matched cohorts were compared using Cox regression. Results: Overall, 11,023 patients initiating PP1M treatment were identified (49.5% were females; mean age of 43.2 [12.2] years). Overall incidences for serious cardiovascular events, expanded serious cardiovascular events, and cardiovascular hospitalizations were 3.92, 7.88 and 51.96 per 1000 person-years, respectively. In matched cohort analysis (N = 10,115), the hazard ratios (HRs) between initiating PP1M and a new oral SGA for serious cardiovascular events, expanded serious cardiovascular events, and cardiovascular hospitalizations were 0.86 (95% confidence interval [CI] 0.55–1.36), 0.88 (95% CI 0.63–1.21), and 0.78 (95% CI 0.69–0.89), respectively. Conclusion: This study reported the population-based incidence of cardiovascular events in schizophrenic patients initiating PP1M treatment. PP1M was not associated with increased risks of serious cardiovascular events but was potentially associated with lower risks of cardiovascular hospitalizations compared with oral SGAs. [ABSTRACT FROM AUTHOR]

BACKGROUND: In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to make the original incomplete datasets become complete. Another type of solution is to directly handle the incomplete datasets without missing value imputation, with decision trees being the major technique for this purpose. OBJECTIVE: To introduce a novel approach, namely Deep Learning-based Decision Tree Ensembles (DLDTE), which borrows the bounding box and sliding window strategies used in deep learning techniques to divide an incomplete dataset into a number of subsets and learning from each subset by a decision tree, resulting in decision tree ensembles. METHOD: Two medical domain problem datasets contain several hundred feature dimensions with the missing rates of 10% to 50% are used for performance comparison. RESULTS: The proposed DLDTE provides the highest rate of classification accuracy when compared with the baseline decision tree method, as well as two missing value imputation methods (mean and k-nearest neighbor), and the case deletion method. CONCLUSION: The results demonstrate the effectiveness of DLDTE for handling incomplete medical datasets with different missing rates. [ABSTRACT FROM AUTHOR]

Data discretization aims to transform a set of continuous features into discrete features, thus simplifying the representation of information and making it easier to understand, use, and explain. In practice, users can take advantage of the discretization process to improve knowledge discovery and data analysis on medical domain problem datasets containing continuous features. However, certain feature values were frequently missing. Many data-mining algorithms cannot handle incomplete datasets. In this study, we considered the use of both discretization and missing-value imputation to process incomplete medical datasets, examining how the order of discretization and missing-value imputation combined influenced performance. The experimental results were obtained using seven different medical domain problem datasets: two discretizers, including the minimum description length principle (MDLP) and ChiMerge; three imputation methods, including the mean/mode, classification and regression tree (CART), and k-nearest neighbor (KNN) methods; and two classifiers, including support vector machines (SVM) and the C4.5 decision tree. The results show that a better performance can be obtained by first performing discretization followed by imputation, rather than vice versa. Furthermore, the highest classification accuracy rate was achieved by combining ChiMerge and KNN with SVM. [ABSTRACT FROM AUTHOR]

Background: Anorexia nervosa (AN) is a serious and potentially life-threatening eating disorder characterized by starvation and malnutrition, a high prevalence of coexisting psychiatric conditions, marked treatment resistance, frequent medical complications, and a substantial risk of death. Body mass index (BMI) is a key measure of treatment outcome of AN and it is necessary to evaluate the long-term prognosis of AN. This study aimed to better assess the BMI course trend between different medications and timepoints in order to improve AN treatment in clinical practice. Methods: During the period 2010–2021, we retrospectively reviewed historical data of all patients diagnosed with AN. There were two groups in this study, which were based on the duration of follow-up. Group A was a 6-month follow-up group, comprising 93 patients (mean age 19.6 ± 6.8 years), with BMI assessed at three consecutive time points: first outpatient visit (T0), three months follow-up (T3), and six months follow-up (T6). Group B was a 12-month follow-up group comprising 36 patients (mean age 17.0 ± 5.2 years) with BMI assessed at five consecutive time points: first outpatient visit (T0), three months follow-up (T3), six months follow-up (T6), nine months follow-up (T9), and twelve months follow-up (T12). In our study, we retrospectively compared BMI courses based on patients’ usage of medication using the following variables: single medication, switching medications, combined medications, and without medications. The primary outcome measurement was BMI recorded at the 6-month follow-up and the 12-month follow-up respectively. In our study, which was conducted at Taichung Veterans General Hospital, we reviewed outpatient medical records of all patients with AN who were seen at the hospital during the period 2010–2021. Results: In Group A (6-month follow-up), patients treated with antidepressants showed a mean BMI increase of 1.3 (p < 0.001); patients treated with antipsychotics showed a mean BMI increase of 1.1 (p = 0.01); patients treated with switching medications showed a mean BMI increase of 0.1 (p = 0.397); patients treated with combined medications showed a mean BMI increase of 0.5 (p = 0.208); and patients treated without medications showed a mean BMI increase of 0.1 (p = 0.821). The results indicated that patients with AN had a significant BMI increase after treatment with antidepressants and antipsychotics in the 6-month follow-up group. In Group B (12-month follow-up), patients treated with antidepressants showed a mean BMI increase of 2.7 (p < 0.001); patients treated with antipsychotics showed a mean BMI increase of 2.8 (p = 0.168); patients treated with switching medications showed a mean BMI decrease of 0.8 (p = 0.595); patients treated with combined medications showed a mean BMI increase of 1.6 (p = 0.368); and patients treated without medications showed a mean BMI increase of 1.0 (p = 0.262). The results indicated that patients with AN had a significant BMI increase after treatment with antidepressants at the 12-month follow-up. Conclusions: AN is a complex disease caused by multiple factors. Evaluating its long-term prognosis is crucial. Our study provides insights and highlights three key findings: 1) medication adherence is crucial in treating AN, 2) frequent switching of medications may not promote weight gain and may also require a re-establishment of rapport with patients with AN, and 3) pharmacotherapy, especially antidepressants, is more effective than no treatment. Further research is needed to confirm these findings. [ABSTRACT FROM AUTHOR]

Cognitive decline is one of the primary concerns in the elderly population. Serious games have been used for different purposes related to elderly care, such as physical therapy, cognitive training and mood management. There has been scientific evidence regarding the relationship between cognition and the autonomic nervous system (ANS) through heart rate variability (HRV). This paper explores the changes in the ANS among elderly people of normal and impaired cognition through measured HRV. Forty-eight subjects were classified into two groups: normal cognition (NC) (n = 24) and mild cognitive impairment (MCI) (n = 24). The subjects went through the following experiment flow: rest for 3 min (Rest 1), play a cognitive aptitude game (Game 1), rest for another 3 min (Rest 2), then play two reaction-time games (Game 2&3). Ten HRV features were extracted from measured electrocardiography (ECG) signals. Based on statistical analysis, there was no significant difference on the HRV between the two groups, but the experiment sessions do have a significant effect. There was no significant interaction between sessions and cognitive status. This implies that the HRV between the two groups have no significant difference, and they will experience similar changes in their HRV regardless of their cognitive status. Based on the game performance, there was a significant difference between the two groups of elderly people. Tree-based pipeline optimization tool (TPOT) was used for generating a machine learning pipeline for classification. Classification accuracy of 68.75% was achieved using HRV features, but higher accuracies of 83.33% and 81.20% were achieved using game performance or both HRV and game performance features, respectively. These results show that HRV has the potential to be used for detection of mild cognition impairment, but game performance can yield better accuracy. Thus, serious games have the potential to be used for assessing cognitive decline among the elderly. [ABSTRACT FROM AUTHOR]

Breast cancer prediction datasets are usually class imbalanced, where the number of data samples in the malignant and benign patient classes are significantly different. Over-sampling techniques can be used to re-balance the datasets to construct more effective prediction models. Moreover, some related studies have considered feature selection to remove irrelevant features from the datasets for further performance improvement. However, since the order of combining feature selection and over-sampling can result in different training sets to construct the prediction model, it is unknown which order performs better. In this paper, the information gain (IG) and genetic algorithm (GA) feature selection methods and the synthetic minority over-sampling technique (SMOTE) are used for different combinations. The experimental results based on two breast cancer datasets show that the combination of feature selection and over-sampling outperform the single usage of either feature selection and over-sampling for the highly class imbalanced datasets. In particular, performing IG first and SMOTE second is the better choice. For other datasets with a small class imbalance ratio and a smaller number of features, performing SMOTE is enough to construct an effective prediction model. [ABSTRACT FROM AUTHOR]

The main purpose of this research is to evaluate the differences in the reaction time and accuracy rate of three categories of subjects using our serious games. Thirty-seven subjects were divided into three groups: normal (n1 = 16), MCI (Mild Cognitive Impairment) (n2 = 10), and dementia—moderate-to-severe (n3 = 11) groups based on the MMSE (Mini Mental State Examination). Two serious games were designed: (1) whack-a-mole and (2) hit-the-ball. Two dependent variables, reaction time and accuracy rate, were statistically analyzed to compare elders' performances in the games among the three groups for three levels of speed: slow, medium, and fast. There were significance differences between the normal group, the MCI group, and the moderate-to-severe dementia group in both the reaction-time and accuracy-rate analyses. We determined that the reaction times of the MCI and dementia groups were shorter compared to those of the normal group, with poorer results also observed in accuracy rate. Therefore, we conclude that our serious games have the feasibility to evaluate reaction performance and could be used in the daily lives of elders followed by clinical treatment in the future. [ABSTRACT FROM AUTHOR]

In this study, a thermal imaging instrument was used to obtain facial thermal image information which was then used to calculate the number of breaths taken. However, small movements were inevitable and the first issue addressed was the means by which image calibration and region selection was to be made. To this end, thermal image sequence data calibration was done using technology that resolved small natural deviations in the nostril area. After these problems had been solved, a Hampel filter was used to process the nostril area signals. The independent component method was used to filter the effects of non‐respiratory signals, and the least squares method was employed for smoothing. Savitzky‐Golay filtering was used to adjust the signal baseline and the processed nostril region thermal image signals were compared with standard abdominal breathing band signals. Results showed that the difference in the number of breaths per minute was less than 1.5 times. The usual normal respiratory frequency range lies within a range of 0.1–0.5Hz. In the calculation of 'coherence obtained from MVAR model', the spectral coherence analysis results showed the methods proposed in this study can substantially enhance the relevance between 0.15 and 0.2Hz. [ABSTRACT FROM AUTHOR]

In practice, many medical domain datasets are incomplete, containing a proportion of incomplete data with missing attribute values. Missing value imputation can be performed to solve the problem of incomplete datasets. To impute missing values, some of the observed data (i.e., complete data) are generally used as the reference or training set, and then the relevant statistical and machine learning techniques are employed to produce estimations to replace the missing values. Since the collected dataset usually contains a certain number of feature dimensions, it is useful to perform feature selection for better pattern recognition. Therefore, the aim of this paper is to examine the effect of performing feature selection on missing value imputation of medical datasets. Experiments are carried out on five different medical domain datasets containing various feature dimensions. In addition, three different types of feature selection methods and imputation techniques are employed for comparison. The results show that combining feature selection and imputation is a better choice for many medical datasets. However, the feature selection algorithm should be carefully chosen in order to produce the best result. Particularly, the genetic algorithm and information gain models are suitable for lower dimensional datasets, whereas the decision tree model is a better choice for higher dimensional datasets. [ABSTRACT FROM AUTHOR]

Digoxin is a high-alert medication because of its narrow therapeutic range and high drug-to-drug interactions (DDIs). Approximately 50% of digoxin toxicity cases are preventable, which motivated us to improve the treatment outcomes of digoxin. The objective of this study is to apply machine learning techniques to predict the appropriateness of initial digoxin dosage. A total of 307 inpatients who had their conditions treated with digoxin between 2004 and 2013 at a medical center in Taiwan were collected in the study. Ten independent variables, including demographic information, laboratory data, and whether the patients had CHF were also noted. A patient with serum digoxin concentration being controlled at 0.5–0.9 ng/mL after his/her initial digoxin dosage was defined as having an appropriate use of digoxin; otherwise, a patient was defined as having an inappropriate use of digoxin. Weka 3.7.3, an open source machine learning software, was adopted to develop prediction models. Six machine learning techniques were considered, including decision tree (C4.5), k-nearest neighbors (kNN), classification and regression tree (CART), randomForest (RF), multilayer perceptron (MLP), and logistic regression (LGR). In the non-DDI group, the area under ROC curve (AUC) of RF (0.912) was excellent, followed by that of MLP (0.813), CART (0.791), and C4.5 (0.784); the remaining classifiers performed poorly. For the DDI group, the AUC of RF (0.892) was the best, followed by CART (0.795), MLP (0.777), and C4.5 (0.774); the other classifiers’ performances were less than ideal. The decision tree-based approaches and MLP exhibited markedly superior accuracy performance, regardless of DDI status. Although digoxin is a high-alert medication, its initial dose can be accurately determined by using data mining techniques such as decision tree-based and MLP approaches. Developing a dosage decision support system may serve as a supplementary tool for clinicians and also increase drug safety in clinical practice. [ABSTRACT FROM AUTHOR]

Many real-world medical datasets contain some proportion of missing (attribute) values. In general, missing value imputation can be performed to solve this problem, which is to provide estimations for the missing values by a reasoning process based on the (complete) observed data. However, if the observed data contain some noisy information or outliers, the estimations of the missing values may not be reliable or may even be quite different from the real values. The aim of this paper is to examine whether a combination of instance selection from the observed data and missing value imputation offers better performance than performing missing value imputation alone. In particular, three instance selection algorithms, DROP3, GA, and IB3, and three imputation algorithms, KNNI, MLP, and SVM, are used in order to find out the best combination. The experimental results show that that performing instance selection can have a positive impact on missing value imputation over the numerical data type of medical datasets, and specific combinations of instance selection and imputation methods can improve the imputation results over the mixed data type of medical datasets. However, instance selection does not have a definitely positive impact on the imputation result for categorical medical datasets. [ABSTRACT FROM AUTHOR]

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers. [ABSTRACT FROM AUTHOR]

Patients with schizophrenia suffer from symptoms such as hallucination and delusion. There are currently a number of publications that discuss the treatment, diagnosis, prognosis, and damage in schizophrenia. This study utilized joint independent component analysis to process the images of GMV and WMV and incorporated the Wisconsin card sorting test (WCST) and the positive and negative syndrome scale (PANSS) to examine the correlation of obtained brain characteristics. We also used PANSS score to classify schizophrenic patients into acute and subacute cases, to analyze the brain structure differences. Finally, we used brain structure images and the error rate of the WCST as eigenvalues in support vector machine learning and classification. The results of this study showed that the frontal and temporal lobes of a normal brain are more apparent than those of a schizophrenia brain. The highest level of classification recognition reached 91.575%, indicating that the WCST error rate and characteristic changes in brain structure volume can be used to effectively distinguish schizophrenia and normal brains. Similarly, this result confirmed that the WCST and brain structure volume are correlated with the differences between schizophrenia and normal participants. [ABSTRACT FROM AUTHOR]

While there is an ample amount of medical information available for data mining, many of the datasets are unfortunately incomplete - missing relevant values needed by many machine learning algorithms. Several approaches have been proposed for the imputation of missing values, using various reasoning steps to provide estimations from the observed data. One of the important steps in data mining is data preprocessing, where unrepresentative data is filtered out of the data to be mined. However, none of the related studies about missing value imputation consider performing a data preprocessing step before imputation. Therefore, the aim of this study is to examine the effect of two preprocessing steps, feature and instance selection, on missing value imputation. Specifically, eight different medical-related datasets are used, containing categorical, numerical and mixed types of data. Our experimental results show that imputation after instance selection can produce better classification performance than imputation alone. In addition, we will demonstrate that imputation after feature selection does not have a positive impact on the imputation result. [ABSTRACT FROM AUTHOR]

Background: Previous research has suggested a link between antidepressants use and the development of cerebrovascular events, but there has never been any study investigating the risk of stroke in obsessive-compulsive disorder (OCD) patients treated with a selective serotonin reuptake inhibitor (SSRI). Methods: A retrospective observational cohort study was conducted using data from the National Health Insurance Database of Taiwan between the year of 2001 and 2009. A total of 527 OCD patients with 412 subjects in the SSRI use group and 115 in the non SSRI use group were included. Multivariable Cox proportional-hazards models were used to explore the associations between SSRI use and the occurrence of stroke, controlling for age, gender, concomitant medications, and comorbid medical illnesses. Results: A total of nineteen OCD patients were diagnosed with new onset of stroke during the follow-up period including six cases in the SSRI group and thirteen in the non SSRI use group. SSRI use was demonstrated to be associated with a decreased risk of stroke (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.10–0.86, P = 0.02). The increase in age-related risk of strokes was 2.55 per decade (HR = 2.55; 95% CI = 1.74–3.75, P<0.001). Alternatively, sex, concomitant use of aspirin and non-steroidal anti-inflammatory drugs, and comorbidities with angina pectoris, diabetes mellitus, hypertension, and hyperlipidemia were not found to be associated with an increased risk for stroke in OCD patients. Conclusions: Our study showed that SSRI use was associated with decreased risk of stroke in OCD patients. Further investigation into the possible biological mechanisms underlying the relationship between stroke and SSRI use in OCD patients is warranted. [ABSTRACT FROM AUTHOR]

Introduction: K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output. Case description: Since the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually. Discussion and evaluation: The experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets. Conclusions: In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best. [ABSTRACT FROM AUTHOR]

Classification is one of the most important technologies used in data mining. Researchers have recently proposed several classification techniques based on the concept of association rules (also known as CBA-based methods). Experimental evaluations on these studies show that in average the CBA-based approaches can yield higher accuracy than some of conventional classification methods. However, conventional CBA-based methods adopt a single threshold of minimum support for all items, resulting in the rare item problem. In other words, the classification rules will only contain frequent items if minimum support ( minsup) is set as high or any combinations of items are discovered as frequent if minsup is set as low. To solve this problem, this paper proposes a novel CBA-based method called MMSCBA, which considers the concept of multiple minimum supports (MMSs). Based on MMSs, different classification rules appear in the corresponding minsups. Several experiments were conducted with six real-world datasets selected from the UCI Machine Learning Repository. The results show that MMSCBA achieves higher accuracy than conventional CBA methods, especially when the dataset contains rare items. [ABSTRACT FROM AUTHOR]

This randomized controlled trial was designed to evaluate the effectiveness of using crisis coping cards (n = 32) in the case management of suicide prevention compared with case management without the use of coping cards (n = 32) over a 3-month intervention period. The generalized estimating equation was used to examine the interaction effect between treatments and time on suicide risk, depression, anxiety, and hopelessness. Results indicated that subsequent suicidal behaviors, severity of suicide risk, depression, anxiety, and hopelessness were reduced more in the coping card intervention group compared to the case management only group. Moreover, for the survival curves of time to suicide reattempt, the coping card group showed a significantly longer time to reattempt than the case management only group at 2-month and 3-month intervention periods. [ABSTRACT FROM AUTHOR]

Background: Irritable bowel syndrome (IBS) is the most common functional gastrointestinal (GI) disorder observed in patients who visit general practitioners for GI-related complaints. A high prevalence of psychiatric comorbidities, particularly anxiety and depressive disorders, has been reported in patients with IBS. However, a clear temporal relationship between IBS and psychiatric disorders has not been well established. Objective: We explored the relationship between IBS and the subsequent development of psychiatric disorders including schizophrenia, bipolar disorder, depressive disorder, anxiety disorder, and sleep disorder. Methods: We selected patients who were diagnosed with IBS caused by gastroenteritis, according to the data in the Taiwan National Health Insurance Research Database. A comparison cohort was formed of patients without IBS who were matched according to age and sex. The incidence rate and the hazard ratios (HRs) of subsequent new-onset psychiatric disorders were calculated for both cohorts, based on psychiatrist diagnoses. Results: The IBS cohort consisted of 4689 patients, and the comparison cohort comprised 18756 matched control patients without IBS. The risks of depressive disorder (HR = 2.71, 95% confidence interval [CI] = 2.30–3.19), anxiety disorder (HR = 2.89, 95% CI = 2.42–3.46), sleep disorder (HR = 2.47, 95% CI = 2.02–3.02), and bipolar disorder (HR = 2.44, 95% CI = 1.34–4.46) were higher in the IBS cohort than in the comparison cohort. In addition, the incidence of newly diagnosed depressive disorder, anxiety disorder, and sleep disorder remained significantly increased in all of the stratified follow-up durations (0–1, 1–5, ≥5 y). Conclusions: IBS may increase the risk of subsequent depressive disorder, anxiety disorder, sleep disorder, and bipolar disorder. The risk ratios are highest for these disorders within 1 year of IBS diagnosis, but the risk remains statistically significant for more than 5 years. Clinicians should pay particular attention to psychiatric comorbidities in IBS patients. [ABSTRACT FROM AUTHOR]

This study develops a hierarchical indicator system for measuring the transport sustainability of Taiwan. An indicator generator is constructed to generate 89 initial indicators. Subsequently, 26 key indicators are selected using rough sets theory (RST). Two-stage principal component analysis (TSPCA) is applied to aggregate the information from 19 key indicators using available historical data to determine the transport sustainability of Taiwan from 1993 to 2010. The results reveal that transport sustainability fluctuated from 1993 to 2005, and then gradually moved toward sustainability after 2005. However, analysis reveals that sustainability in social aspect is in a long-term decline. [ABSTRACT FROM AUTHOR]

Background: Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders among women of reproductive age. A higher prevalence of psychiatric comorbidities, including depressive disorder, anxiety disorder, and bipolar disorder has been proved in patients with PCOS. However, a clear temporal causal relationship between PCOS and psychiatric disorders has not been well established. Objective: We explored the relationship between PCOS and the subsequent development of psychiatric disorders including schizophrenia, bipolar disorder, depressive disorder, anxiety disorder, and sleep disorder. Methods: We identified patients who were diagnosed with PCOS by an obstetrician-gynecologist in the Taiwan National Health Insurance Research Database. A comparison cohort was constructed of patients without PCOS who were matched according to age and sex. The occurrence of subsequent new-onset psychiatric disorders was evaluated in both cohorts based on diagnoses made by psychiatrists. Results: The PCOS cohort consisted of 5431 patients, and the comparison cohort consisted of 21,724 matched control patients without PCOS. The incidence of depressive disorder (hazard ratio [HR] 1.296, 95% confidence interval [CI] 1.084–.550), anxiety disorder (HR 1.392, 95% CI 1.121–1.729), and sleep disorder (HR 1.495, 95% CI 1.176–1.899) were higher among the PCOS patients than among the patients in the comparison cohort. In addition, a higher incidence of newly diagnosed depressive disorder, anxiety disorder, and sleep disorder remained significantly increased in all of the stratified follow-up durations (0–1, 1–5, ≥5 y). Conclusions: PCOS might increase the risk of subsequent newly diagnosed depressive disorder, anxiety disorder, and sleep disorder. The risk of newly diagnosed bipolar disorder, which has often been reported in the literature to be comorbid with PCOS, was not significantly elevated. [ABSTRACT FROM AUTHOR]

Background & Aims: To evaluate the risk of depressive disorders among non-alcoholic patients by using the Taiwan National Health Insurance Research Database (NHIRD). Methods: We conducted a retrospective study of a matched cohort of 52 725 participants (10 545 non-alcoholic cirrhotic patients and 42 180 control patients) who were selected from the NHIRD. Patients were observed for a maximum of 11 years to determine the rates of newly onset depressive disorders, and Cox regression was used to identify the risk factors associated with depressive disorders in cirrhotic patients. Results: During the 11-year follow-up period, 395 (3.75%) non-alcoholic cirrhotic patients and 1 183 (2.80%) control patients were diagnosed with depressive disorders. The incidence risk ratio of depressive disorders between non-alcoholic cirrhotic patients and control patients was 1.76 (95% CI, 1.57–1.98, P<.001). After adjusting for age, sex, and comorbidities, non-alcoholic cirrhotic patients were 1.75 times more likely to develop depressive disorders (95% CI, 1.56–1.96, P<.001) compared with the control patients. The hazard ratios for patients younger than 60 years old (1.31) and female (1.25) indicated that each is an independent risk factor for depressive disorders in non-alcoholic cirrhotic patients. Conclusions: The likelihood of developing depressive disorders is greater among non-alcoholic cirrhotic patients than among patients without cirrhosis. Symptoms of depression should be sought in patients with cirrhosis. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the risk of cancer among patients with generalized anxiety disorder (GAD) in a nationwide population-based dataset. Methods: We recruited newly-diagnosed GAD patients aged 20 years or older without antecedent cancer from the Taiwan National Health Insurance Research database between 2000–2010. Standardized incidence ratios (SIRs) of cancers were calculated in GAD patients, and the subgroup of GAD patients diagnosed by psychiatric specialists. Results: A total of 559 cancers developed among 19,793 GAD patients with a follow-up of 89,485 person-years (median follow-up of 4.34 years), leading to a significantly increased SIR of 1.14 [95% confidence interval (CI) 1.05–1.24]. Male GAD patients had a significantly increased SIR overall (1.30, 95% CI 1.15–1.46) and for lung and prostate cancer (1.77, 95% CI 1.33–2.30 and 2.17, 95% CI 1.56–2.93, respectively). Patients over 80 years of age also had a significantly increased SIR (1.56, 95% CI 1.25–1.92), especially in males. However, psychiatrist-diagnosed GAD patients did not show increased cancer risk relative to the general population, perhaps due to having fewer physical comorbidities than non-psychiatrist-diagnosed GAD patients. Conclusion: This study found that overall cancer risk is elevated among patients with GAD. The risk of lung and prostate cancer also increased in male patients with GAD. This increased cancer risk may be due to physical comorbidities and surveillance bias. Further prospective study is necessary to confirm these findings. [ABSTRACT FROM AUTHOR]

As the proportion of elderly people continues to grow, so does the concern about age-related cognitive decline. Serious games have been developed for cognitive training or treatment, but measuring the activity of the autonomic nervous system (ANS) has not been taken to account. However, cognitive functioning has been known to be heavily influenced by the autonomic nervous system (ANS), and ANS activity can be quantified using heart rate variability (HRV). This paper aims to analyze the physiological response in normal elderly people as they play two types of serious games using HRV features from electrocardiography (ECG). A wearable device designed in-house was used to measure ECG, and the data from this device was pre-processed using digital signal processing techniques. Ten HRV features were extracted, including time-domain, nonlinear, and frequency-domain features. The experiment proceeds as follows: rest for three minutes, play a cognitive aptitude game, rest for another three minutes, followed by two reaction time games. Data from thirty older adults (age: 65.9 ± 7.34; male: 15, female: 15) were analyzed. The statistical results show that there was a significant difference in the HRV between the two types of games. From this, it can be concluded that the type of game has a significant effect on the ANS response. This can be further used in designing games for the elderly, either for training or mood management. [ABSTRACT FROM AUTHOR]

For decades, scientists have been trying to solve the problem of dementia, with no cure currently available. Semantic–lexical impairment is well established as the early critical sign of dementia, although there are still gaps in knowledge that must be investigated. In this study, we used fMRI to observe the neural activity of 31 subjects, including 16 HC (Healthy Control) and 15 AD (Alzheimer's Disease), who participated in the naming task. The neuropsychology profile of HC (Healthy Control) and AD (Alzheimer's Disease) are discussed in this study. The involvement of FG (Fusiform Gyrus) and IFG (Inferior Frontal Gyrus) shows dominant activation in both of the groups. We observed a decrease in neural activity in the AD group, resulting in semantic deficit problems in this preliminary study. Furthermore, ROI analysis was performed and revealed both hyperactivation and hypoactivation in the AD group. The compensatory mechanism demonstrated during the task, due to the effort required to identify an animal's name, represents the character profile of AD. [ABSTRACT FROM AUTHOR]

Despite an increasing number of reports on the associations between chronic occupational stress and structural and functional changes of the brain, the underlying neural correlates of perceived occupational stress is still not clear. Perceived stress reflects the extents to which situations are appraised as stressful at a given point in one's life. Using near-infrared spectroscopy, we investigated the associations between perceived occupational stress and cortical activity over the bilateral frontotemporal regions during a verbal fluency test. Sixty-eight participants (17 men, 51 women), 20-62 years of age were recruited. Perceived occupational stress was measured using the Chinese version of Job Content Questionnaire, and the Chinese version of the Copenhagen Burnout Inventory. We found statistically significant negative associations between occupational burnout and brain cortical activity over the fronto-polar and dorsolateral prefrontal cortex during the VFT (r = −0.343 to −0.464). In conclusion, our research demonstrated a possible neural basis of perceived occupational stress that are distributed across the prefrontal cortex. [ABSTRACT FROM AUTHOR]

Background: Late-life depression is common among elderly patients. Ignorance of the health problem, either because of under-diagnosis or under-treatment, causes additional medical cost and comorbidity. For a better health and quality of life (QoL), evaluation, prevention and treatment of late-life depression in elderly patients is essential.Methods: This study examined (1) the differences of clinical characteristics, degree of improvement on QoL and functionality on discharge between non-depressed and depressed elderly inpatients and (2) factors associated with QoL on discharge. Four hundred and seventy-one elderly inpatients admitted to a geriatric evaluation and management unit (GEMU) from 2009 to 2010 were enrolled in this study. Comprehensive geriatric assessment including the activities of daily living (ADL), geriatric depression scale, and mini-mental state examination were conducted. QoL was assessed using the European Quality of Life-5 Dimensions and the European Quality of Life-5 Dimensions Visual Analog Scale on discharge. Information on hospital stay and Charlson comorbidity index were obtained by chart review. Chi-square tests, independent t-tests, Mann-Whitney U tests and multiple linear regressions were used in statistical analysis.Results: Worse QoL and ADL on discharge were found among the depressed. Depressive symptoms, female gender, duration of hospital stay, and rehabilitation were significant factors affecting QoL on discharge in linear regression models.Conclusions: The importance of the diagnosis and treatment of depression among elderly inpatients should not be overlooked during hospital stay and after discharge. Greater efforts should be made to improve intervention with depressed elderly inpatients. [ABSTRACT FROM AUTHOR]