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One of the biggest challenges for requesters and platform providers of crowdsourcing is quality control, which is to expect high-quality results from crowd workers. We propose an unsupervised statistical quality estimation method for general crowdsourcing tasks with unstructured response formats such as article writing, program coding, and logo designing, which occupy the majority on most crowdsourcing marketplaces.

BackgroundThe prevalence of non-communicable diseases is increasing throughout the world, including developing countries.ObjectiveThe intent was to conduct a study of a preventive medical service in a developing country, combining eHealth checkups and teleconsultation as well as assess stratification rules and the short-term effects of intervention.MethodsWe developed an eHealth system that comprises a set of sensor devices in an attaché case, a data transmission system linked to a mobile network, and a data management application. We provided eHealth checkups for the populations of five villages and the employees of five factories/offices in Bangladesh. Individual health condition was automatically categorized into four grades based on international diagnostic standards: green (healthy), yellow (caution), orange (affected), and red (emergent). We provided teleconsultation for orange- and red-grade subjects and we provided teleprescription for these subjects as required.ResultsThe first checkup was provided to 16,741 subjects. After one year, 2361 subjects participated in the second checkup and the systolic blood pressure of these subjects was significantly decreased from an average of 121 mmHg to an average of 116 mmHg (P<.001). Based on these results, we propose a cost-effective method using a machine learning technique (random forest method) using the medical interview, subject profiles, and checkup results as predictor to avoid costly measurements of blood sugar, to ensure sustainability of the program in developing countries.ConclusionsThe results of this study demonstrate the benefits of an eHealth checkup and teleconsultation program as an effective health care system in developing countries.

Background: Predicting networks of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of available data has enabled applications of computational approaches such as statistical modeling and machine learning method. Both a set of chemical interactions and chemical compound structures are represented as graphs, and various graph-based approaches including graph convolutional neural networks have been successfully applied to chemical network prediction. However, there was no efficient method that can consider the two different types of graphs in an end-to-end manner. Results: We give a new formulation of the chemical network prediction problem as a link prediction problem in a graph of graphs (GoG) which can represent the hierarchical structure consisting of compound graphs and an inter-compound graph. We propose a new graph convolutional neural network architecture called dual graph convolutional network that learns compound representations from both the compound graphs and the inter-compound network in an end-to-end manner. Conclusions: Experiments using four chemical networks with different sparsity levels and degree distributions shows that our dual graph convolution approach achieves high prediction performance in relatively dense networks, while the performance becomes inferior on extremely-sparse networks.

Clonidine hydrochloride has been used for pre-anesthetic medication to provide a pre-operative sedation in pediatric surgery. The purpose of this study is to determine the plasma clonidine concentration, which gives satisfactory sedation in pediatric surgery. Sixteen pediatric patients (age: 1-11 years, weight: 9-33 kg) received either 2 or 4 microg/kg of clonidine lollipop before entering the operating room. Plasma clonidine concentrations were determined 120 min after administration of clonidine lollipop. Pre-operative sedation was evaluated by 5-point scoring systems at entering the operating room. The changes in systolic blood pressure (SBP), diastolic blood pressure (DBP) and heart rate (HR) were also assessed before and after administration of clonidine lollipop. The patients with satisfactory sedation had higher plasma clonidine concentration than that of the patients with unsatisfactory sedation (0.45+/-0.16 ng/ml vs. 0.26+/-0.16 ng/ml, p<0.05). The clonidine concentrations in the satisfactory group ranged from 0.28 to 0.81 ng/ml. There was no significant difference in hemodynamic parameters (SBP, DBP and HR) before and after administration of clonidine lollipop in both satisfactory and unsatisfactory sedation groups. Plasma clonidine concentration of 0.3-0.8 ng/ml would be sufficient to produce satisfactory sedation without changes in hemodynamic parameters in pediatric surgery.