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The oxygen saturation is one of the important parameters to evaluate human health. This paper presents an efficient optimization method that can improve the accuracy of oxygen saturation measurement, which employs an optical frequency division triangular wave signal as the excitation signal to obtain dynamic spectrum and calculate oxygen saturation. In comparison to the traditional method measured RMSE (root mean square error) of SpO2 which is 0.1705, this proposed method significantly reduced the measured RMSE which is 0.0965. It is notable that the accuracy of oxygen saturation measurement has been improved significantly. The method can simplify the circuit and bring down the demand of elements. Furthermore, it has a great reference value on improving the signal to noise ratio of other physiological signals. [ABSTRACT FROM AUTHOR]

Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. [ABSTRACT FROM AUTHOR]

Abstract: In response to the changing nature of health issues, standardized health ontologies such as SNOMED CT and UMLS incline to change more frequently than most other domain ontologies. Yet, semantic interoperability shared among institutions within a distributed health care enterprise relies heavily on the availability of a valid and up-to-date standardized ontology. In this paper, we propose the creation and preservation of sub-ontologies to deal with the frequent changes in health ontologies. Our approach focuses on the nature and characteristics of standard health ontologies, however it can also be applied to other domain ontologies with similar characteristics. Our sub-ontology evolution approach defines ways to create valid sub-ontologies for each specific health application, and to effectively develop a series of propagation mechanism when the main ontology changes. Our approach will (i) isolate the required change propagation to the relevant health applications that utilized the changing concepts only, and (ii) optimize the propagation mechanism to include the minimum number of operations only. Since a sub-ontology should be a valid ontology by itself, the change propagation approach used in this process should contain the rules to assure the validity of the produced sub-ontology while keeping the consistency of the sub-ontology to the evolved base ontology. A change identification process, which considers the nature of the health ontology change logs, is conducted to identify the semantics of the changes. From the evaluation, it is shown that the content of the evolved sub-ontologies produced using our approach is consistent to the evolved base ontology. Moreover, the propagation process can be performed more efficiently because the number of operations required for our change propagation method is lower than the number of operations required for direct re-extraction from the evolved base ontology. [Copyright &y& Elsevier]