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Background: MiRNA is a class of non-coding single-stranded RNA molecules with a length of approximately 22 nucleotides encoded by endogenous genes, which can regulate the expression of other genes. Therefore, it is very important to predict the associations between miRNA and disease. Predecessors developed a new prediction method of drug-disease association, and it achieved good results.Methods: In this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. First, we integrate three associations into a heterogeneous network, such as the known miRNA-disease association, miRNA-miRNA similarities and disease-disease similarities, next we apply graph convolution network to learn the embedding of miRNA and disease. We use an attention mechanism to combine embedding from multiple convolution layers. Unobserved miRNA-disease associations are scored based on integrated embedding.Results: After fivefold cross-validations, the value of AUC is reached 0.9091, which is higher than other prediction methods and baseline methods.Conclusions: In this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. LAGCN has achieved good performance in predicting miRNA-disease associations, and it is superior to other association prediction methods and baseline methods. [ABSTRACT FROM AUTHOR]

Background: Even though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions.Methods: We simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) × 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability.Results: Our results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges.Conclusion: Employing simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain. [ABSTRACT FROM AUTHOR]

Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a useful perspective on contemporary causal inference methods, and (ii) explore the role of machine learning (as one approach to 'best prediction') in causal inference. Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to describe and test novel computational driver models, predicting drivers' brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC).Background: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving.Method: Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers' arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study.Results: The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study.Conclusion: Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data.Application: Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving. [ABSTRACT FROM AUTHOR]

Background: Electronic medical records contain a variety of valuable medical information for patients. So, when we are able to recognize and extract risk factors for disease from EMRs of patients with cardiovascular disease (CVD), and are able to use them to predict CVD, we have the ability to automatically process clinical texts, resulting in an improved accuracy of supporting doctors for the clinical diagnosis of CVD. In the case where CVD is becoming more worldwide, predictive CVD based on EMRs has been studied by many researchers to address this important aspect of improving diagnostic efficiency.Methods: This paper proposes an Enhanced Character-level Deep Convolutional Neural Networks (EnDCNN) model for cardiovascular disease prediction.Results: On the manually annotated Chinese EMRs corpus, our risk factor identification extraction model achieved 0.9073 of F-score, our prediction model achieved 0.9516 of F-score, and the prediction result is better than the most previous methods.Conclusions: The character-level model based on text region embedding can well map risk factors and their labels as a unit into a vector, and downsampling plays a crucial role in improving the training efficiency of deep CNN. What's more, the shortcut connections with pre-activation used in our model architecture implements dimension-matching free in training. [ABSTRACT FROM AUTHOR]

Background: The accelerated growth of elderly population is creating a heavy burden to the healthcare system in many developed countries and regions. Electrocardiogram (ECG) analysis has been recognized as effective approach to cardiovascular disease diagnosis and widely utilized for monitoring personalized health conditions.Method: In this study, we present a novel approach to forecasting one-day-forward wellness conditions for community-dwelling elderly by analyzing single lead short ECG signals acquired from a station-based monitoring device. More specifically, exponentially weighted moving-average (EWMA) method is employed to eliminate the high-frequency noise from original signals at first. Then, Fisher-Yates normalization approach is used to adjust the self-evaluated wellness score distribution since the scores among different individuals are skewed. Finally, both deep learning-based and traditional machine learning-based methods are utilized for building wellness forecasting models.Results: The experiment results show that the deep learning-based methods achieve the best fitted forecasting performance, where the forecasting accuracy and F value are 93.21% and 91.98% respectively. The deep learning-based methods, with the merit of non-hand-crafted engineering, have superior wellness forecasting performance towards the competitive traditional machine learning-based methods.Conclusion: The developed approach in this paper is effective in wellness forecasting for community-dwelling elderly, which can provide insights in terms of implementing a cost-effective approach to informing healthcare provider about health conditions of elderly in advance and taking timely interventions to reduce the risk of malignant events. [ABSTRACT FROM AUTHOR]

In order to address the flight delays and risks associated with the forecasted increase in air traffic, there is a need to increase the capacity of air traffic management systems. This should be based on objective measurements of traffic situation complexity. In current air traffic complexity research, no simple means is available to integrate airspace and traffic flow characteristics. In this paper, we propose a new approach for the measurement of air traffic situation complexity. This approach considers the effects of both airspace and traffic flow and objectively quantifies air traffic situation complexity. Considering the aircraft, waypoints, and airways as nodes, and the complexity relationships among these nodes as edges, a dynamic weighted network is constructed. Air traffic situation complexity is defined as the sum of the weights of all edges in the network, and the relationships of complexity with some commonly used indices are statistically analyzed. The results indicate that the new complexity index is more accurate than traffic count and reflects the number of trajectory changes as well as the high-risk situations. Additionally, analysis of potential applications reveals that this new index contributes to achieving complexity-based management, which represents an efficient method for increasing airspace system capacity. [ABSTRACT FROM AUTHOR]

Background: In the past 20 years, the trends of ischemic heart disease (IHD) mortality in China have been described in divergent claims. This research analyzes mortality trends for IHD by using the data from 102 continuous Disease Surveillance Points (DSP) from 1991 to 2009.Method: The 102 continuous DSP covered 7.3 million people during the period 1991-2000, and then were expanded to a population of 52 million in the same areas for 2004-2009. The data were adjusted by using garbage code redistribution and underreporting rate, mapped from international classification of diseases ICD-9 to ICD-10. The mortality rates for IHD were further adjusted by the crude death proportion multiplied by the total number of deaths in the mortality envelope, which was calculated by using logrt = a + bt. Age-standard death rates (ASDRs) were computed using China's 2010 census population structure. Trend in IHD was calculated from ASDRs by using a joinpoint regression model.Results: The IHD ASDRs increased in total in regions with an average annual percentage change (AAPC) 4.96%, especially for the Southwest (AAPC = 7.97%) and Northeast areas (AAPC = 7.10%), and for male and female subjects (with 5% AAPC) as well. In rural areas, the year 2000 was a cut-off point for mortality rate with annual percentage change increasing from 3.52% in 1991-2000 to 9.02% in 2000-2009, which was much higher than in urban areas (AAPC = 1.05%). And the proportion of deaths increased in older adults, and more male deaths occurred before age 60 compared to female deaths.Conclusion: By observing a wide range of areas across China from 1991 to 2009, this paper concludes that the ASDR trend for IHD increased. These trends reflect changes in the Chinese standard of living and lifestyle with diets higher in fat, higher blood lipids and increased body weight. [ABSTRACT FROM AUTHOR]

Background: Norway has achieved a noteworthy reduction in smoking prevalence over the past forty years. In 2015, 13% of Norwegians aged 13-74 smoked daily and a further 9% smoked occasionally. One of the objectives of the Norwegian 2013-16 national strategy for tobacco control is to achieve a reduction in the daily smoking prevalence to<10% by 2016. This paper aims to estimate how long it will take for Norway to achieve the 10% smoking prevalence. Methods: A dynamic forecasting model using smoking prevalence data from national survey data on the prevalence of current, former and never smokers in the Norwegian population from 1985 to 2012 was used to estimate future smoking prevalence in the Norwegian population based on a continuation of current patterns in smoking cessation and initiation rates. Results: The result suggests that Norway's smoking prevalence among men and women will continue to decline if current trends of smoking initiation and cessation continue. Our model predicts, based on figures for 1985-2012, that the prevalence of current daily and occasional smoking among men and women will not fall below the 10% mark until 2029 for men and 2026 for women, if current trends continue. Conclusion: Smoking is likely to remain an important public health issue in Norway for many years. New strategies are needed to accelerate the decline in smoking in Norway. [ABSTRACT FROM AUTHOR]

The culture trap is the tendency to put cultural markers and habits above the demands of reason or compassion. It can reduce receptivity to new ideas and trigger Phoenix Cycles of catastrophe and renaissance. System research is then complicated by the historiographic problem of continuity and change, because there are no objective criteria for deciding whether "the system" survived or was destroyed by the catastrophe. This paper explores the differences between uncertainty emergence and self-organizing emergence using the concept of a "possibility space" to clarify the relationship between anti-causal events and causal states, i.e., the meso-history of conjuncture. Conjunctures are interpreted ex post in the context of deep time. The paper distinguishes autopoiesis, i.e., a new instance of a species, from elaboration, i.e., the origin of new species. It also describes a spectrum ranging from metastability, in which the possibility space is time invariant, to innovation, in which the possibility space is a synergetic artefact of changing habits and beliefs. Metastable systems are computably complex; we can make predictions across the boundaries of successive conjunctures, subject to statistical uncertainties. Innovative systems are uncomputably complex; although we can make predictions within a conjuncture and anticipate bottlenecks, we cannot predict beyond the conjuncture because conceptual taxonomies will not be conserved. The relationship between adaptive potential and constraint remains fundamental, although debates about whether the system is resilient are meaningless. System boundaries are artefacts of a dynamic consensus. The analytical sciences use methods designed for metastable systems, whereas the discursive sciences presume innovation. These methods predispose researchers to different space-time perspectives that, in turn, make it possible to generalize about their roles in different policy arenas. Tensions between analytical and discursive scientists become more understandable and manageable if these historiographic issues are clarified. The relationship between innovation and metastability is illustrated in an appendix on the emergence of social complexity in Europe. [ABSTRACT FROM AUTHOR]

This paper examines the paper of Haux et al. in this issue of this journal. It gives some background on specifics of the German health care system, which underlie the theses and prognoses proposed by Haux et al. In analogy to a forecast of the future of health informatics, which is now 10 years old, I then suggest that these attempts meet two types of challenges:•that of overestimating the positive effects of recent advances, which later are found not to scale up; and•that of blind spots with respect to unforeseen significant advances.The attempt to find indications of such in the projections of Haux et al. leads, among other, to the conclusion that the projections of direct linkages between patients and care providers may be overoptimistic. As to wheather the deviating opinions matter in the end, it is concluded that the technology advances may require less attention that the restructuring of the health care system required to take advantage of the advances of technology. [Copyright &y& Elsevier]

Great progress has been made in mathematical models of cholera transmission dynamics in recent years. However, little impact, if any, has been made by models upon public health decision-making and day-to-day routine of epidemiologists. This paper provides a brief introduction to the basics of ordinary differential equation models of cholera transmission dynamics. We discuss a basic model adapted from Codeço (2001), and how it can be modified to incorporate different hypotheses, including the importance of asymptomatic or inapparent infections, and hyperinfectious V. cholerae and human-to-human transmission. We highlight three important challenges of cholera models: (1) model misspecification and parameter uncertainty, (2) modeling the impact of water, sanitation and hygiene interventions and (3) model structure. We use published models, especially those related to the 2010 Haitian outbreak as examples. We emphasize that the choice of models should be dictated by the research questions in mind. More collaboration is needed between policy-makers, epidemiologists and modelers in public health. [ABSTRACT FROM AUTHOR]

Background: During the global pandemic of A/H1N1/California/07/2009 (A/H1N1/Cal) influenza, many governments signed contracts with vaccine producers for a universal influenza immunization program and bought hundreds of millions of vaccines doses. We argue that, as Health Ministers assumed the occurrence of the worst possible scenario (generalized pandemic influenza) and followed the strong version of the Precautionary Principle, they undervalued the possibility of mild or weak pandemic wave. Methodology: An alternative decision rule, based on the non-extensive entropy principle, is introduced, and a different Precautionary Principle characterization is applied. This approach values extreme negative results (catastrophic events) in a different way and predicts more plausible and mild events. It introduces less pessimistic forecasts in the case of uncertain influenza pandemic outbreaks. A simplified application is presented using seasonal data of morbidity and severity among Italian children influenza-like illness for the period 2003–10. Principal Findings: Established literature results predict an average attack rate of not less than 15% for the next pandemic influenza [Meltzer M, Cox N, Fukuda K. The economic impact of pandemic influenza in the United States: implications for setting priorities for interventions. Emerg Infect Dis 1999;5:659–71; Meltzer M, Cox N, Fukuda K. Modeling the Economic Impact of Pandemic Influenza in the United States: Implications for Setting Priorities for Intervention. Background paper. Atlanta, GA: CDC, 1999. Available at: http://www.cdc.gov/ncidod/eid/vol5no5/melt_back.htm (7 January 2011, date last accessed))]. The strong version of the Precautionary Principle would suggest using this prediction for vaccination campaigns. On the contrary, the non-extensive maximum entropy principle predicts a lower attack rate, which induces a 20% saving in public funding for vaccines doses. Conclusions: The need for an effective influenza pandemic prevention program, coupled with an efficient use of public funding, calls for a rethinking of the Precautionary Principle. The non-extensive maximum entropy principle, which incorporates vague and incomplete information available to decision makers, produces a more coherent forecast of possible influenza pandemic and a conservative spending in public funding. [ABSTRACT FROM PUBLISHER]

Patients' experience of stereotype threat in clinical settings and encounters may be one contributor to health care disparities. Stereotype threat occurs when cues in the environment make negative stereotypes associated with an individual's group status salient, triggering physiological and psychological processes that have detrimental consequences for behavior. By recognizing and understanding the factors that can trigger stereotype threat and understanding its consequences in medical settings, providers can prevent it from occurring or ameliorate its consequences for patient behavior and outcomes. In this paper, we discuss the implications of stereotype threat for medical education and trainee performance and offer practical suggestions for how future providers might reduce stereotype threat in their exam rooms and clinics. [ABSTRACT FROM AUTHOR]

Applying information and communication technology (ICT) to a given medical domain is not merely adding a new technique. When applied to a medical domain, ICT has the potential to radically change processes in that domain. In their paper ‘Health care in the information society: a prognosis for the year 2013’ Haux and co-workers provide us with a set of predictions based on progress in three main areas: (1) patient-centred recording and use of medical data for collaborative care; (2) process-integrated decision support; and (3) comprehensive use of patient data for research and health care reporting. We complement their theses by predicting some of the feedback mechanisms that will develop as ICT is shaping health care. Feedback is return to the input of a part of the output of a mechanism; this part of the input constituting information that reports discrepancies between intended and actual operation and leads to a self-correcting action that can be utilised. We discuss feedback in the areas Haux identifies: the patient record, decision support, and the use of data for research and health care reporting. For each of these domains, we will discuss the output that serves as input, the discrepancies discovered, and the self-correction that will or should follow. [Copyright &y& Elsevier]

It is well-known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper, we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as Data Salvage. We show, for data from two companies, how our approach-based upon expert judgement-adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practicing project manager. From this empirical work, we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction. [ABSTRACT FROM AUTHOR]

This paper presents predictions regarding traffic safety research. It seems that, absent some extraordinary development, traffic in 20 years time will not look all that different from 1993 traffic, or 1970 traffic. There will, of course, be myriads of detailed technical improvements. Grand electronic systems will provide many small benefits in terms of convenience, comfort, and more especially information. With regards to traffic safety research, many of the reasons that have prevented traffic safety research from acquiring more of the values and methods of science are related to the institutional framework in which it is generally pursued. Funding tends to be for specific projects, even though the goal of research is to find things not previously known.

Patients with advanced cancer are burdened physically and psychologically, so there is an urgent need to pay more attention to their health-related quality of life (HRQOL). With an expected clinical endpoint prediction, over-treatment can be effectively eliminated by the means of palliative care at the right time. This paper develops a deep learning based approach for cancer clinical endpoint prediction based on patient's electronic health records (EHR). Due to the pervasive existence of categorical information in EHR, it brings unavoidably obstacles to the effective numerical learning algorithms. To address this issue, we propose a novel cross-field categorical attributes embedding (CCAE) model to learn a vectorized representation for cancer patients in attribute-level by orders, in which the strong semantic coupling among categorical variables are well exploited. By transforming the order-dependency modeling into a sequence learning task in an ingenious way, recurrent neural network is adopted to capture the semantic relevance among multi-order representations. Experimental results from the SEER-Medicare EHR dataset have illustrated that the proposed model can achieve competitive prediction performance compared with other baselines. [ABSTRACT FROM AUTHOR]

Forensic anthropology is an increasingly developing discipline born about a century ago in the United States with the objective to contribute the knowledge of bone biology and physical anthropology to the emerging needs of the court of law. The development of research in biological and forensic anthropology has made rapid progress worldwide in the past few years, however, in most countries--with the exception of the United States--forensic anthropology work is still considered within the duties of the forensic pathologist. This paper attempts to summarise the history and development of forensic anthropology in Thailand by providing information on past and current research and practice that can help forensic practitioners to apply existing methods in forensic cases and mass disasters. It is hoped that the lessons learned from the tsunami catastrophe and the emerging need for positive identification in medicolegal settings will lead to rapid advances in education, training and professional engagement of anthropologists from the forensic departments and the law enforcement agencies in Thailand. [ABSTRACT FROM AUTHOR]

The ability to incorporate and manage the different drivers of land-use change in a modeling process is one of the key challenges because they are complex and are both quantitative and qualitative in nature. This paper uses Bayesian belief networks (BBN) to incorporate characteristics of land managers in the modeling process and to enhance our understanding of land-use change based on the limited and disparate sources of information. One of the two models based on spatial data represented land managers in the form of a quantitative variable, the area of individual holdings, whereas the other model included qualitative data from a survey of land managers. Random samples from the spatial data provided evidence of the relationship between the different variables, which I used to develop the BBN structure. The model was tested for four different posterior probability distributions, and results showed that the trained and learned models are better at predicting land use than the uniform and random models. The inference from the model demonstrated the constraints that biophysical characteristics impose on land managers; for older land managers without heirs, there is a higher probability of the land use being arable agriculture. The results show the benefits of incorporating a more complex notion of land managers in land-use models, and of using different empirical data sources in the modeling process. Future research should focus on incorporating more complex social processes into the modeling structure, as well as incorporating spatio-temporal dynamics in a BBN. [ABSTRACT FROM AUTHOR]