Background: Using insecticides is considered as a temporary and rapid method for interrupting the disease transmission through disease vectors such as dengue. The insecticide Fendona, when absorbed into bed nets, is slowly released and rapidly knocks out, kills or repels mosquitoes as they come into contact with the bed net. Objectives: To evaluate the effectiveness of the Interceptor bed net (a unique textile-finishing product developed by BASF, Singapore PTE. LTD, that were coated with the Fendona insecticide) on controlling disease vector Aedes aegypti. Subjects and method: An experimental trial was carried out at the National Institute of Hygiene and Epidemiology and in the field of Hien Giang commune, Thuong Tin district, Ha Tay province. The changes of efficacy of Interceptor bed net were compared by not washing with washing at the different levels. Results: The unwashed bed nets can knock out and kill Aedes aegypti mosquitoes by 50% after 20 minutes of exposure on every trial. About 90-100% of total mosquitoes were killed after the first 30 minutes of exposure. Bed net\u2019s efficacy remains until 24 hours of the trial. The recovery rate of the mosquito is 0%. The same results were also observed after 90 days, 36 days and 18 days of bed nets being laundered. However, after 9 day of washing, the bed nets can only kill the mosquitoes up to about 90-95% of the total after the first 30 minutes of exposure. It also reached 100% of killing after 24 hours. However, its efficacy was conversely reduced with regard to the increase of laundry times. Conclusion: The density of adult mosquitoes and larvae for Aedes aegypti was significantly reduced in the intervention areas, even at zero level in comparison to the control sites. The insecticide had no side-effect or any stimulation to users, community, poultry, cattle and environment in the intervention sites.
Interceptor bednet ; disease vector ; Aedes aegypti

Objectives: This scoping review aimed to support a landscape analysis to identify lessons learned about intersectoral collaborations (ISCs) by describing their existing models in the context of dengue, malaria and yellow fever. Methods: A scoping review following the methodology of Joanna Briggs Institute was performed using the following inclusion criteria: studies involving humans; studies discussing intersectoral collaborations, malaria/dengue/yellow fever, and prevention or control at any level; and studies in countries endemic for the aforementioned diseases. Studies were screened using Covidence, while data were extracted using NVivo. Results: Of the 7,535 records retrieved, 69 were included in the qualitative analysis. Most ISCs were initiated by multilateral organizations and ministries of health, and none by communities. Strategies included advocacy, health education, research, public health measures, resource mobilization, service delivery and training; mostly employed on a community level. Monitoring and evaluation were mostly formative, ongoing, and participatory. Gaps included administrative and policy barriers, resource shortages, and inadequate research and training. Conclusions Multiple models of ISC exist in the literature. There is a need to develop a comprehensive framework for an effective and sustainable multisectoral approach for the prevention and control of VBDs ensuring adequate resources, active stakeholders, and strategies that span the entire socio-ecological spectrum.
Dengue ; Disease Vectors ; Intersectoral Collaboration ; Malaria ; Vector Borne Diseases

OBJECTIVETo develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint.

METHODSFour types of objective diagnostic data were collected from 835 CHD patients by using a self-developed CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm (REAL).

RESULTSREAL was employed to establish a Xin (Heart) qi defificiency, Xin yang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively.

CONCLUSIONSThe multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.

OBJECTIVE: To identify potential imaging biomarkers of Alzheimer's disease by combining brain cortical thickness (CThk) and functional connectivity and to validate this model's diagnostic accuracy in a validation set. MATERIALS AND METHODS: Data from 98 subjects was retrospectively reviewed, including a study set (n = 63) and a validation set from the Alzheimer's Disease Neuroimaging Initiative (n = 35). From each subject, data for CThk and functional connectivity of the default mode network was extracted from structural T1-weighted and resting-state functional magnetic resonance imaging. Cortical regions with significant differences between patients and healthy controls in the correlation of CThk and functional connectivity were identified in the study set. The diagnostic accuracy of functional connectivity measures combined with CThk in the identified regions was evaluated against that in the medial temporal lobes using the validation set and application of a support vector machine. RESULTS: Group-wise differences in the correlation of CThk and default mode network functional connectivity were identified in the superior temporal (p < 0.001) and supramarginal gyrus (p = 0.007) of the left cerebral hemisphere. Default mode network functional connectivity combined with the CThk of those two regions were more accurate than that combined with the CThk of both medial temporal lobes (91.7% vs. 75%). CONCLUSION: Combining functional information with CThk of the superior temporal and supramarginal gyri in the left cerebral hemisphere improves diagnostic accuracy, making it a potential imaging biomarker for Alzheimer's disease.
Alzheimer Disease* ; Biomarkers ; Brain ; Cerebrum ; Humans ; Magnetic Resonance Imaging ; Neuroimaging* ; Parietal Lobe ; Retrospective Studies ; Support Vector Machine ; Temporal Lobe

Base on the data of blood pressure, plasma lipid, Glu and UA by physical test, Support Vector Machine (SVM) was applied to identify coronary heart disease (CHD) in patients and non-CHD individuals in south China population for guide of further prevention and treatment of the disease. Firstly, the SVM classifier was built using radial basis kernel function, liner kernel function and polynomial kernel function, respectively. Secondly, the SVM penalty factor C and kernel parameter sigma were optimized by particle swarm optimization (PSO) and then employed to diagnose and predict the CHD. By comparison with those from artificial neural network with the back propagation (BP) model, linear discriminant analysis, logistic regression method and non-optimized SVM, the overall results of our calculation demonstrated that the classification performance of optimized RBF-SVM model could be superior to other classifier algorithm with higher accuracy rate, sensitivity and specificity, which were 94.51%, 92.31% and 96.67%, respectively. So, it is well concluded that SVM could be used as a valid method for assisting diagnosis of CHD.
Algorithms ; China ; Coronary Artery Disease ; diagnosis ; Discriminant Analysis ; Humans ; Models, Theoretical ; Neural Networks (Computer) ; Sensitivity and Specificity ; Software ; Support Vector Machine

OBJECTIVES: The aim of this study was to establish a prediction model of medication adherence in elderly patients with chronic diseases and to identify variables showing the highest classification accuracy of medication adherence in elderly patients with chronic diseases using support vector machine (SVM) and conventional statistical methods, such as logistic regression (LR). METHODS: We included 293 chronic disease patients older than 65 years treated at one tertiary hospital. For the medication adherence, Morisky's self-report was used. Data were collected through face-to-face interviews. The mean age of the patients was 73.8 years. The classification process was performed with LR (SPSS ver. 20.0) and SVM (MATLAB ver. 7.12) method. RESULTS: Taking into account 16 variables as predictors, the result of applying LR and SVM classification accuracy was 71.1% and 97.3%, respectively. We listed the top nine variables selected by SVM, and the accuracy using a single variable, self-efficacy, was 72.4%. The results suggest that self-efficacy is a key factor to medication adherence among a Korean elderly population both in LR and SVM. CONCLUSIONS: Medication non-adherence was strongly associated with self-efficacy. Also, modifiable factors such as depression, health literacy, and medication knowledge associated with medication non-adherence were identified. Since SVM builds an optimal classifier to minimize empirical classification errors in discriminating between patient samples, it could achieve a higher accuracy with the smaller number of variables than the number of variables used in LR. Further applications of our approach in areas of complex diseases, treatment will provide uncharted potentials to researchers in the domains.
Aged ; Chronic Disease ; Depression ; Health Literacy ; Humans ; Logistic Models ; Medication Adherence ; Regression Analysis ; Support Vector Machine ; Tertiary Care Centers

The occurrence of acute hypotensive episodes (AHE) in intensive care units (ICU) seriously endangers the lives of patients, and the treatment is mainly depended on the expert experience of doctors. In this paper, a model for predicting the occurrence of AHE in ICU has been developed using the theory of medical Informatics. We analyzed the trend and characteristics of the mean arterial blood pressure (MAP) between the patients who were suffering AHE and those who were not, and extracted the median, mean and other statistical parameters for learning and training based on support vector machine (SVM), then developed a predicting model. On this basis, we also compared different models consisted of different kernel functions. Experiments demonstrated that this approach performed well on classification and prediction, which would contribute to forecast the occurrence of AHE.
Acute Disease ; Diagnosis, Computer-Assisted ; methods ; Forecasting ; Humans ; Hypotension ; diagnosis ; Intensive Care Units ; Models, Cardiovascular ; Support Vector Machine

Aiming at three kinds of tremor, including essential tremor (ET), Parkinsonian disease (PD) tremor and physiological tremor (PT), which are subjected to frequent clinical misdiagnosis, a new method based on singular value decomposition (SVD) of empirical mode decomposition (EMD) and support vector machine (SVM) for the recognition of tremor is proposed in this paper. First, the hand acceleration signals of three different types of 40 tremor voluntary subjects were collected on the basis of informed consent, and the EMD method was used to decompose the signals into a number of stationary intrinsic mode functions (IMFs). Then the preceding four IMFs which could describe signals were selected, and the initial feature vector matrixes were formed. After the application of SVD technique to the initial feature vector matrixes, the singular values were obtained and used as the feature vectors of tremor types to be put in the support vector machine classifier as well as in the identification of tremor types. The results of experiment have shown that the proposed diagnosis method based on SVD of EMD and SVM can extract tremor features effectively and identify tremor types accurately. It also provides a new assistant approach for clinical diagnosis of tremor.
Algorithms ; Arm ; physiopathology ; Diagnosis, Computer-Assisted ; Diagnosis, Differential ; Electromyography ; methods ; Humans ; Muscle, Skeletal ; physiology ; Parkinson Disease ; diagnosis ; Signal Processing, Computer-Assisted ; Software ; Support Vector Machine ; Tremor ; diagnosis

OBJECTIVE: This study proposes an automated diagnostic method to classify patients with Alzheimer's disease (AD) of degenerative etiology using magnetic resonance imaging (MRI) markers. METHODS: Twenty-seven patients with subjective memory impairment (SMI), 18 patients with mild cognitive impairment (MCI), and 27 patients with AD participated. MRI protocols included three dimensional brain structural imaging and diffusion tensor imaging to assess the cortical thickness, subcortical volume and white matter integrity. Recursive feature elimination based on support vector machine (SVM) was conducted to determine the most relevant features for classifying abnormal regions and imaging parameters, and then a factor analysis for the top-ranked factors was performed. Subjects were classified using nonlinear SVM. RESULTS: Medial temporal regions in AD patients were dominantly detected with cortical thinning and volume atrophy compared with SMI and MCI patients. Damage to white matter integrity was also accredited with decreased fractional anisotropy and increased mean diffusivity (MD) across the three groups. The microscopic damage in the subcortical gray matter was reflected in increased MD. Classification accuracy between pairs of groups (SMI vs. MCI, MCI vs. AD, SMI vs. AD) and among all three groups were 84.4% (+/-13.8), 86.9% (+/-10.5), 96.3% (+/-4.6), and 70.5% (+/-11.5), respectively. CONCLUSION: This proposed method may be a potential tool to diagnose AD pathology with the current clinical criteria.
Alzheimer Disease* ; Anisotropy ; Atrophy ; Brain ; Classification* ; Diagnosis ; Diffusion Tensor Imaging ; Humans ; Magnetic Resonance Imaging ; Memory ; Mild Cognitive Impairment ; Pathology ; Support Vector Machine

OBJECTIVES: Predicting the length of stay (LOS) of patients in a hospital is important in providing them with better services and higher satisfaction, as well as helping the hospital management plan and managing hospital resources as meticulously as possible. We propose applying data mining techniques to extract useful knowledge and draw an accurate model to predict the LOS of heart patients. METHODS: Data were collected from patients with coronary artery disease (CAD). The patient records of 4,948 patients who had suffered CAD were included in the analysis. The techniques used are classification with three algorithms, namely, decision tree, support vector machines (SVM), and artificial neural network (ANN). LOS is the target variable, and 36 input variables are used for prediction. A confusion matrix was obtained to calculate sensitivity, specificity, and accuracy. RESULTS: The overall accuracy of SVM was 96.4% in the training set. Most single patients (64.3%) had an LOS < or =5 days, whereas 41.2% of married patients had an LOS >10 days. Moreover, the study showed that comorbidity states, such as lung disorders and hemorrhage with drug consumption have an impact on long LOS. The presence of comorbidities, an ejection fraction <2, being a current smoker, and having social security type insurance in coronary artery patients led to longer LOS than other subjects. CONCLUSIONS: All three algorithms are able to predict LOS with various degrees of accuracy. The findings demonstrated that the SVM was the best fit. There was a significant tendency for LOS to be longer in patients with lung or respiratory disorders and high blood pressure.
Comorbidity ; Coronary Artery Disease ; Coronary Vessels ; Data Mining ; Decision Trees ; Heart ; Hemorrhage ; Humans ; Hypertension ; Insurance ; Length of Stay ; Lung ; Sensitivity and Specificity ; Social Security ; Support Vector Machine