Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) for the diagnosis of dementia within community dwelling populations

<b>Background</b><p></p> Various tools exist for initial assessment of possible dementia with no consensus on the optimal assessment method. Instruments that use collateral sources to assess change in cognitive function over time may have particular utility. The most commonly used informant dementia assessment is the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE).<p></p> A synthesis of the available data regarding IQCODE accuracy will help inform cognitive assessment strategies for clinical practice, research and policy.<p></p> <b>Objectives</b><p></p> Our primary objective was to determine the diagnostic accuracy of the informant based questionnaire IQCODE, for detection of all cause (undifferentiated) dementia in community-dwelling adults with no previous cognitive assessment. We sought to describe the accuracy of IQCODE (the index test) against a clinical diagnosis of dementia (the reference standard).<p></p> Our secondary objective was to describe the effect of heterogeneity on the summary estimates. We were particularly interested in the traditional 26-item scale versus the 16-item short form; and language of administration. We explored the effect of varying the threshold IQCODE score used to define 'test positivity'.<p></p> <b>Search methods</b><p></p> We searched the following sources on 28 January 2013: ALOIS (Cochrane Dementia and Cognitive Improvement Group), MEDLINE (OvidSP), EMBASE (OvidSP), PsycINFO (OvidSP), BIOSIS Previews (ISI Web of Knowledge), Web of Science with Conference Proceedings (ISI Web of Knowledge), LILACS (BIREME). We also searched sources relevant or specific to diagnostic test accuracy: MEDION (Universities of Maastrict and Leuven); DARE (York University); ARIF (Birmingham University). We used sensitive search terms based on MeSH terms and other controlled vocabulary.<p></p> <b>Selection criteria</b><p></p> We selected those studies performed in community settings that used (not necessarily exclusively) the IQCODE to assess for presence of dementia and, where dementia diagnosis was confirmed, with clinical assessment. Our intention with limiting the search to a 'community' setting was to include those studies closest to population level assessment. Within our predefined community inclusion criteria, there were relevant papers that fulfilled our definition of community dwelling but represented a selected population, for example stroke survivors. We included these studies but performed sensitivity analyses to assess the effects of these less representative populations on the summary results.<p></p> <b>Data collection and analysis</b><p></p> We screened all titles generated by the electronic database searches and abstracts of all potentially relevant studies were reviewed. Full papers were assessed for eligibility and data extracted by two independent assessors. For quality assessment (risk of bias and applicability) we used the QUADAS 2 tool. We included test accuracy data on the IQCODE used at predefined diagnostic thresholds. Where data allowed, we performed meta-analyses to calculate summary values of sensitivity and specificity with corresponding 95% confidence intervals (CIs). We pre-specified analyses to describe the effect of IQCODE format (traditional or short form) and language of administration for the IQCODE.<p></p> <b>Main results</b><p></p> From 16,144 citations, 71 papers described IQCODE test accuracy. We included 10 papers (11 independent datasets) representing data from 2644 individuals (n = 379 (14%) with dementia). Using IQCODE cut-offs commonly employed in clinical practice (3.3, 3.4, 3.5, 3.6) the sensitivity and specificity of IQCODE for diagnosis of dementia across the studies were generally above 75%.<p></p> Taking an IQCODE threshold of 3.3 (or closest available) the sensitivity was 0.80 (95% CI 0.75 to 0.85); specificity was 0.84 (95% CI 0.78 to 0.90); positive likelihood ratio was 5.2 (95% CI 3.7 to 7.5) and the negative likelihood ratio was 0.23 (95% CI 0.19 to 0.29).<p></p> Comparative analysis suggested no significant difference in the test accuracy of the 16 and 26-item IQCODE tests and no significant difference in test accuracy by language of administration. There was little difference in sensitivity across our predefined diagnostic cut-points.<p></p> There was substantial heterogeneity in the included studies. Sensitivity analyses removing potentially unrepresentative populations in these studies made little difference to the pooled data estimates. The majority of included papers had potential for bias, particularly around participant selection and sampling. The quality of reporting was suboptimal particularly regarding timing of assessments and descriptors of reproducibility and inter-observer variability.<p></p> <b>Authors' conclusions</b><p></p> Published data suggest that if using the IQCODE for community dwelling older adults, the 16 item IQCODE may be preferable to the traditional scale due to lesser test burden and no obvious difference in accuracy. Although IQCODE test accuracy is in a range that many would consider 'reasonable', in the context of community or population settings the use of the IQCODE alone would result in substantial misdiagnosis and false reassurance. Across the included studies there were issues with heterogeneity, several potential biases and suboptimal reporting quality

Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning

<p>Abstract</p> <p>Background</p> <p>Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem.</p> <p>Methods</p> <p>Standardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets.</p> <p>Results</p> <p>A total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.</p> <p>Conclusions</p> <p>The novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.</p

Science and Technology Governance and Ethics - A Global Perspective from Europe, India and China

This book analyzes the possibilities for effective global governance of science in Europe, India and China. Authors from the three regions join forces to explore how ethical concerns over new technologies can be incorporated into global science and technology policies. The first chapter introduces the topic, offering a global perspective on embedding ethics in science and technology policy. Chapter Two compares the institutionalization of ethical debates in science, technology and innovation policy in three important regions: Europe, India and China. The third chapter explores public perceptions of science and technology in these same three regions. Chapter Four discusses public engagement in the governance of science and technology, and Chapter Five reviews science and technology governance and European values. The sixth chapter describes and analyzes values demonstrated in the constitution of the People’s Republic of China. Chapter Seven describes emerging evidence from India on the uses of science and technology for socio-economic development, and the quest for inclusive growth. In Chapter Eight, the authors propose a comparative framework for studying global ethics in science and technology. The following three chapters offer case studies and analysis of three emerging industries in India, China and Europe: new food technologies, nanotechnology and synthetic biology. Chapter 12 gathers all these threads for a comprehensive discussion on incorporating ethics into science and technology policy. The analysis is undertaken against the backdrop of different value systems and varying levels of public perception of risks and benefits. The book introduces a common analytical framework for the comparative discussion of ethics at the international level. The authors offer policy recommendations for effective collaboration among the three regions, to promote responsible governance in science and technology and a common analytical perspective in ethics

Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

New techniques to characterise the vaginal microbiome in pregnancy

Understanding of the vaginal microbiome in health and disease is essential to screen, detect and manage complications in pregnancy. One of the major complications of pregnancy is preterm birth, which is the leading world-wide cause of death and disability in children under five years of age. The aetiology of preterm birth is multifactorial, but a causal link has been established with infection. Despite the importance of understanding the vaginal microbiome in pregnancy in order to evaluate strategies to prevent and manage PTB, currently used culture based techniques provide limited information as not all pathogens are able to be cultured. The implementation of culture-independent high-throughput techniques and bioinformatics tools are advancing our understanding of the vaginal microbiome. New methods employing 16S rRNA and metagenomics analyses make possible a more comprehensive description of the bacteria of the human microbiome. Several studies on the vaginal microbiota of pregnant women have identified a large number of taxa. Studies also suggest reduced diversity of the microbiota in pregnancy compared to non-pregnant women, with a relative enrichment of the overall abundance of Lactobacillus species, and significant differences in the diversity of Lactobacillus spp. A number of advantages and disadvantages of these techniques are discussed briefly. The potential clinical importance of the new techniques is illustrated through recent reports where traditional culture-based techniques failed to identify pathogens in high risk complicated pregnancies whose presence subsequently was established using culture-independent, high-throughput analyses