The performance of risk prediction models for pre-eclampsia using routinely collected maternal characteristics and comparison with models that include specialised tests and with clinical guideline decision rules: a systematic review. BJOG 2016 Aug;123(9):1441-1452 [FREE Full text] [doi: 10.1111/1471...
multiple runs of the base algorithm to construct a stabilized e-value, which leads to higher Power without loss of stability. It is very general and can be applied to almost all FDR control method, such as knockoffs, data splitting methods. Theoretical properties of this stability method are ...
“With AI, we can develop more accurate disease and phenotype prediction models through the in-depth analysis of desensitized gene data.” This, she says, will help us build a more detailed genetic structure of the population and hopefully find new drug targets. For example, non-invasive ...
In many practical application time series have heavy-tailed noise that significantly impacts the performance of classical forecasting models; in particular, accurately modeling a distribution over extreme events is crucial to performing accurate time series anomaly detection. We propose a Spliced Binned-Par...
provide a benchmark for predictive accuracy trained only on prior clinical actions and indicate that models with similar performance may derive their signal by looking over clinician’s shoulders—using clinical behavior as the expression of preexisting intuition and suspicion to generate a prediction. ...
Nomograms are graphical tools that utilize statistical prediction model to present and enhance the comprehension of clinical prediction models [15]. Despite their wide-ranging utilization, there are limited nomograms currently accessible for predicting the risk of AKI in patients suffering from acute pan...
The article analyzes the relationship between financial distress measures and the markets' perception of risk or bond yield spread to determine the relative performance of these models. Databases used to analyze the impact of bankruptcy prediction models on the cost of debt financing, included the Leh...
We turn now to simulations for three major reasons. First, since we do not know the small sample statistical properties of our forecast performance measures, simulations are required to know whether the above results are specific to the HO data or to be expected for this class of models. Seco...
However, their potential for solving the simulation-optimization problems of construction projects has not been investigated. This research contributes by investigating the status-quo of simulation-optimization models in the construction field and comparing the performance of five recent swarm intelligence ...
1.2. Study on Traffic Accident Risk Prediction Generally, predicting the risk of traffic accidents requires a large amount of traffic flow data. By mining and extracting the characteristics of traffic accidents and establishing prediction models, it is possible to predict traffic accident risks in a ...