Slow delayed rectifying potassium current (IKs) - analysis of thein vitroinhibition data and predictive model development

2012 ◽  
Vol 33 (8) ◽  
pp. 723-739 ◽  
Author(s):  
Sebastian Polak ◽  
Barbara Wiśniowska ◽  
Anna Glinka ◽  
Kamil Fijorek ◽  
Aleksander Mendyk
Keyword(s):  
Predictive Model ◽  
Potassium Current ◽  
Model Development

scholarly journals Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region

2020 ◽  
Vol 51 (4) ◽  
pp. 648-665
Author(s):  
Min Wu ◽  
Qi Feng ◽  
Xiaohu Wen ◽  
Ravinesh C. Deo ◽  
Zhenliang Yin ◽  
...  
Keyword(s):  
Random Forest ◽  
Uncertainty Analysis ◽  
Predictive Model ◽  
Air Temperature ◽  
Model Development ◽  
Influential Factor ◽  
Weather Data ◽  
Data Sets ◽  
Oasis Area ◽  
Lower Uncertainty

Abstract The study evaluates the potential utility of the random forest (RF) predictive model used to simulate daily reference evapotranspiration (ET0) in two stations located in the arid oasis area of northwestern China. To construct an accurate RF-based predictive model, ET0 is estimated by an appropriate combination of model inputs comprising maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine durations (Sun), wind speed (U2), and relative humidity (Rh). The output of RF models are tested by ET0 calculated using Penman–Monteith FAO 56 (PMF-56) equation. Results showed that the RF model was considered as a better way to predict ET0 for the arid oasis area with limited data. Besides, Rh was the most influential factor on the behavior of ET0, except for air temperature in the proposed arid area. Moreover, the uncertainty analysis with a Monte Carlo method was carried out to verify the reliability of the results, and it was concluded that RF model had a lower uncertainty and can be used successfully in simulating ET0. The proposed study shows RF as a sound modeling approach for the prediction of ET0 in the arid areas where reliable weather data sets are available, but relatively limited.

scholarly journals Landfill Methane Oxidation: Predictive Model Development

2015 ◽  
Vol 15 (2) ◽  
pp. 283-288 ◽  
Author(s):  
M.F.M. Abushammal ◽  
N.E.A. Basri ◽  
M.K. Younes
Keyword(s):  
Predictive Model ◽  
Methane Oxidation ◽  
Model Development

scholarly journals Causal Models for the Result of Percutaneous Coronary Intervention in Coronary Chronic Total Occlusions

2021 ◽  
Vol 11 (19) ◽  
pp. 9258
Author(s):  
Maria Ganopoulou ◽  
Ioannis Kangelidis ◽  
Georgios Sianos ◽  
Lefteris Angelis
Keyword(s):  
Predictive Model ◽  
Causal Structure ◽  
Model Development ◽  
Coronary Intervention ◽  
Causal Models ◽  
Percutaneous Coronary Interventions ◽  
Graph Structure ◽  
Chronic Total Occlusions ◽  
Operator Experience ◽  
Percutaneous Coronary

Background: Patients undergoing coronary angiography very frequently exhibit coronary chronic total occlusions (CTOs). Over the last decade, there has been an increasing acceptance of the percutaneous coronary interventions (PCI) in CTOs due to, among else, rising operator experience and advances in technology. This study is an effort to address the problem of identifying important factors related to the success or failure of the PCI. Methods: The analysis is based on the EuroCTO Registry, which is the largest database available worldwide, consisting of 164 variables and 29,995 cases for the period 2008–2018. The aim is to assess the dynamics of causal models and causal discovery, using observational data, in predicting the result of the PCI. Causal models use graph structure to assess the cause–effect relationships between variables. In this study, the constrained-based algorithm PC was employed. The focus was to find the local causal structure around the PCI result and use it as a feature selection tool for building a predictive model. Results: The model developed was compared with other modeling approaches from the literature, and it was found to perform equally well or better. Conclusions: The analysis showcased the potential of employing local causal structure in predictive model development.

Modeling Length of Stay as an Optimized Two-class Prediction Problem

2007 ◽  
Vol 46 (03) ◽  
pp. 352-359 ◽  
Author(s):  
N. Peek ◽  
F. Voorbraak ◽  
E. de Jonge ◽  
B. A. J. M. de Mol ◽  
M. Verduijn
Keyword(s):  
Length Of Stay ◽  
Predictive Model ◽  
Model Development ◽  
Outcome Variable ◽  
Prediction Problem ◽  
Threshold Values ◽  
Class Prediction ◽  
Common Strategy ◽  
Risk Patients ◽  
Prediction Problems

Summary Objectives: To develop a predictive model for the outcome length of stay at the Intensive Care Unit (ICU LOS), including the choice of an optimal dichotomization threshold for this outcome. Reduction of prediction problems of this type of outcome to a two-class problem is a common strategy to identify high-risk patients. Methods: Threshold selection and model development are performed simultaneously. From the range of possible threshold values, the value is chosen for which the corresponding predictive model has maximal precision based on the data. To compare the precision of models for different dichotomizations of the outcome, the MALOR performance statistic is introduced. This statistic is insensitive to the prevalence of positive cases in a two-class prediction problem. Results: The procedure is applied to data from cardiac surgery patients to dichotomize the outcome ICU LOS. The class probabilitytree method is used to develop predictive models. Within our data, the best model precision is found at the threshold of seven days. Conclusions: The presented method extends existing procedures for predictive modeling with optimization of the outcome definition for predictive purposes. The method can be applied to all prediction problems where the outcome variable needs to be dichotomized, and is insensitive to changes in the prevalence of positive cases with different dichotomization thresholds.

scholarly journals Prediction of psychosis: model development and internal validation of a personalized risk calculator

2020 ◽  
pp. 1-9
Author(s):  
Tae Young Lee ◽  
Wu Jeong Hwang ◽  
Nahrie S. Kim ◽  
Inkyung Park ◽  
Silvia Kyungjin Lho ◽  
...  
Keyword(s):  
High Risk ◽  
Predictive Model ◽  
Help Seeking ◽  
Test Score ◽  
Cox Regression ◽  
Verbal Learning ◽  
Model Development ◽  
Predictive Ability ◽  
Internal Validation ◽  
Early Intervention In Psychosis

Abstract Background Over the past two decades, early detection and early intervention in psychosis have become essential goals of psychiatry. However, clinical impressions are insufficient for predicting psychosis outcomes in clinical high-risk (CHR) individuals; a more rigorous and objective model is needed. This study aims to develop and internally validate a model for predicting the transition to psychosis within 10 years. Methods Two hundred and eight help-seeking individuals who fulfilled the CHR criteria were enrolled from the prospective, naturalistic cohort program for CHR at the Seoul Youth Clinic (SYC). The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was used to develop a predictive model for a psychotic transition. We performed k-means clustering and survival analysis to stratify the risk of psychosis. Results The predictive model, which includes clinical and cognitive variables, identified the following six baseline variables as important predictors: 1-year percentage decrease in the Global Assessment of Functioning score, IQ, California Verbal Learning Test score, Strange Stories test score, and scores in two domains of the Social Functioning Scale. The predictive model showed a cross-validated Harrell's C-index of 0.78 and identified three subclusters with significantly different risk levels. Conclusions Overall, our predictive model showed a predictive ability and could facilitate a personalized therapeutic approach to different risks in high-risk individuals.

scholarly journals Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

2014 ◽  
Vol 05 (03) ◽  
pp. 836-860 ◽  
Author(s):  
D.A. Hanauer ◽  
Y. Huang
Keyword(s):  
Predictive Model ◽  
Error Rate ◽  
Model Development ◽  
Data Sources ◽  
Validation Dataset ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Clinic Efficiency ◽  
The Impact ◽  
Patient Waiting

SummaryBackground: Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity.Objective: To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows.Methods: Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost.Results: From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods.Conclusions: This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient’s show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity.Citation: Huang Y, Hanauer D.A. Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl Clin Inf 2014; 5: 836–860http://dx.doi.org/10.4338/ACI-2014-04-RA-0026

scholarly journals Addressing patient heterogeneity in disease predictive model development

Biometrics ◽  
2021 ◽  
Author(s):  
Xu Gao ◽  
Weining Shen ◽  
Jing Ning ◽  
Ziding Feng ◽  
Jianhua Hu
Keyword(s):  
Predictive Model ◽  
Model Development ◽  
Patient Heterogeneity
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