Sentences with phrase «risk prediction models»

The finding that genetic variation identified by trait GWASs partially captures environmental risk factors or protective factors has direct implications for risk prediction models and the interpretation of GWAS findings.

In another study, Matheny et al. (2005) evaluated the discrimination and calibration of mortality risk prediction models (logistic regression) in interventional cardiology and obtained positive results with the use of this method.

Computational methods for identifying new risk loci, training risk prediction models, and fine - mapping causal variants from summary statistics of genome - wide association studies are playing an increasingly important role in the human genetics community.

Of particular interest to clinicians, the statement emphasizes the benefits of including information on socioeconomic position in cardiovascular risk prediction models.»

Not exact matches

This calls into question the reliability of industry asset allocation and diversification strategies and the prediction capability of conventional portfolio risk modelling techniques.

To assess the robustness of the results of our regression analysis, we performed covariate adjustment with derived propensity scores to calculate the absolute risk difference (details are provided in the Supplementary Appendix, available with the full text of this article at NEJM.org).14, 15 To calculate the adjusted absolute risk difference, we used predictive margins and G - computation (i.e., regression - model — based outcome prediction in both exposure settings: planned in - hospital and planned out - of - hospital birth).16, 17 Finally, we conducted post hoc analyses to assess associations between planned out - of - hospital birth and outcomes (cesarean delivery and a composite of perinatal morbidity and mortality), which were stratified according to parity, maternal age, maternal education, and risk level.

Regardless of what climate models find, investigating these long - distance links in weather could also pay off by improving risk prediction and forecasts.

«When used in conjunction with forecasted data, the model predictions could be useful for focusing both surveillance efforts, and the pre-positioning of material and equipment in areas and periods of particularly high risk.

Upstate Medical University researcher Anna Stewart Ibarra, Ph.D., M.P.A., and her colleagues have created a mathematical model that can serve as a guide to make monthly predictions on when people are at greatest risk for contracting mosquito - borne viruses, such as dengue, Zika and chikungunya, due to climate conditions.

Although hospitals can make risk predictions about when individual asthma patients might return, based on medical histories, the model created by Ram and her collaborators makes predictions at the population level.

You should call this approach the «Risk Generalization - Particularization» model of medical prediction, Jonathan Fuller and Luis Flores write in a paper to be published in Studies in History and Philosophy of Biological and Biomedical Sciences.

The model is simple because its purpose is not an accurate prediction of how best to protect VIPs, but to see what general lessons we can learn about reducing risks and then apply them to more esoteric forms of risk.

Predictions based on the Met Office climate model suggest, «a rise of 400 million in the number of people at risk from hunger», he says.

These quantifications might further help, and indeed convince, decision - makers across the world to decide on wide - scale introduction of prediction models and risk - based management for cardiovascular disease.»

The climate models aren't really good enough in their representation of present - day circulation to give you much confidence in the specifics of their predictions [so that you could use them to do a cost - benefit analysis for example], but the risk of widespread change is still there.

The development and validation of new risk scores with sex - specific weighting of risk factors could be a promising tool for future prediction models.

«Our next steps are to consider how to incorporate a prior false - positive mammogram and biopsy results into risk - prediction models for breast cancer,» she said.

Developing Cell Therapies: Enabling Cost Prediction By Value Systems Modeling to Manage Developmental Risk.

Although the combination of measures improves classification accuracy in a prediction model, in practice, it is difficult to discern the combination of cut scores and patterns of performance that would identify a student as at risk for not passing the criterion measure.

King LG, Wohl JS, Manning AM, Hackner SG, Raffe M, Maislin G. Evaluation of the survival prediction index as a model of risk stratification for clinical research in dogs admitted to intensive care units at four locations.

But the potentially severe impacts of a quickly warming world up the ante; therefore, though the model predictions have significant error bars, a risk management perspective demands that significant mitigations steps be taken immediately.

Emission scenarios and model predictions may overstate the risk, but they are equally likely to underestimate it.There is some evidence that this warming has already begun.

(Kahan et al, 2012, Figure 2) The results quite clearly show that the prediction of the SCT model is falsified and that the perceived risk of climate change is not correlated with science literacy and numeracy.

Are all of the alarmist warmistas in a world - at - risk tizzy over projections of catastrophe by computer models, or are they engaged in making predictions of impending doom, based on models and all manner of other misinterpreted evidence and made up nonsense?

Projections of these changes of risk using models in which changes in the background climate are incorporated, and applied using models that do a fair job at the short time scale (like high resolution weather prediction, or hydrological discharge, or...) is thus a viable procedure, and does yield added value.

Climate Science for Serving Society: Research, Modeling and Prediction Priorities fosters a more effective dialogue between the climate information and knowledge developers — the research community — and decision makers who must respond to difficult adaptation, mitigation and risk management issues.

Furthermore, if ONE Global Climate Model was verified — if it produced useful predictions (that's in advance and all...: — RRB --RRB- I'd be impressed and more likely to consider it a useful tools in unravelling our climate, assessing risk benefits, and in making policy decisions.

Didier Sornette's presentation at the AGU entitled «Dragon - Kings, black swans, and prediction» raises these issues, which need to be considered in the context of risk assessment and economic modeling.

Those worried about the risks of climate change try to use the models to get best possible predictions, while those who oppose for ideological reasons any action tell that you should not give any value to those results.

I'm usually the type of person that doesn't take the risks of CO2 too seriously as far as modelled predictions go.

Yet, faith in these model valuations led to a prediction that Freddie Mac stock was «cheap» when a meltdown of the financial system, largely due to the incorrect valuations and risk estimates by computer models, was less than 180 days away.

adopting models that have not been validated or even hide or underestimate uncertainty in their predictions results in increased risks

This report discusses our current understanding of the mechanisms that link declines in Arctic sea ice cover, loss of high - latitude snow cover, changes in Arctic - region energy fluxes, atmospheric circulation patterns, and the occurrence of extreme weather events; possible implications of more severe loss of summer Arctic sea ice upon weather patterns at lower latitudes; major gaps in our understanding, and observational and / or modeling efforts that are needed to fill those gaps; and current opportunities and limitations for using Arctic sea ice predictions to assess the risk of temperature / precipitation anomalies and extreme weather events over northern continents.

(d) In decision making, adopting models that have not been validated or even hide or underestimate uncertainty in their predictions, results in increased risks.

As destructive as the fire was, it means that the group could collect real - world data to test the validity of predictions from their computer - model synthesis and then adjust it to be more useful to city and county planners responsible for mitigating the risk of post-fire flows.

Next - generation models for estimating extinction risks should incorporate these factors in order to increase biological realism and therefore the accuracy of future predictions.»

Sheltered archipelagos are at risk from ocean level rise, per long term climate model predictions.

The EPOS prediction model improves ability to predict transition to first episode psychosis in individuals at high risk

In line with the transactional model's prediction, a three - way interaction between these factors was found for internalizing and total problem behaviors, suggesting that children, who are more emotionally reactive, experience little maternal responsiveness, are more vulnerable to experience distress, and have learned to interpret mother's ambiguous behavior as unsupportive, are most at risk to display internalizing and total problem behaviors.

These results are somewhat consistent with predictions that could be derived from the Wallander et al. (1989) Risk and Resistance Model.

Clinical risk in infancy marginally contributed to the prediction model.