Uncertainty Analysis Methods for Comparing Predictive Models and Biomarkers: A Case Study of Dietary Methyl Mercury Exposure

1998 ◽  
Vol 28 (2) ◽  
pp. 96-105 ◽  
Author(s):  
Rafael A. Ponce ◽  
Scott M. Bartell ◽  
Terrance J. Kavanagh ◽  
James S. Woods ◽  
William C. Griffith ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Predictive Models ◽  
Methyl Mercury ◽  
Mercury Exposure ◽  
Analysis Methods

scholarly journals A Case Study in View of Developing Predictive Models for Water Supply System Management

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3305
Author(s):  
Katarzyna Pietrucha-Urbanik ◽  
Barbara Tchórzewska-Cieślak ◽  
Mohamed Eid
Keyword(s):  
Water Supply ◽  
Predictive Models ◽  
Failure Modes ◽  
Supply System ◽  
Water Supply System ◽  
System Management ◽  
System A ◽  
Operational Failure ◽  
Operational Data

Initiated by a case study to assess the effectiveness of the modernisation actions undertaken in a water supply system, some R&D activities were conducted to construct a global predictive model, based on the available operational failure and recovery data. The available operational data, regarding the water supply system, are the pipes’ diameter, failure modes, materials, functional conditions, seasonality, and the number of failures and time-to-recover intervals. The operational data are provided by the water company responsible of the supply system. A predictive global model is proposed based on the output of the operational data statistical assessment. It should assess the expected effectiveness of decisions taken in support of the modernisation and the extension plan.

Evaluating prediction of continuous clinical values: a glucose case study (Preprint)

2021 ◽  
Author(s):  
George Hripcsak ◽  
David J Albers
Keyword(s):  
Predictive Models ◽  
Clinical Utility ◽  
Clinical Care ◽  
Glucose Measurement ◽  
Care Processes ◽  
Low Glucose ◽  
Potential Clinical Utility ◽  
Rms Error ◽  
Error Grid

BACKGROUND Background: It would be useful to be able to assess the utility of predictive models of continuous values before clinical trials are carried out. OBJECTIVE Objective: To compare metrics to assess the potential clinical utility of models that produce continuous value forecasts. METHODS Methods: We ran a set of data assimilation forecast algorithms on time series of glucose measurements from intensive care unit patients. We evaluated the forecasts using four sets of metrics: glucose root mean square error, a set of metrics on a transformed glucose value, the estimated effect on clinical care based on an insulin guideline, and a glucose measurement error grid (Parkes grid). We assessed correlation among the metrics and created a set of factor models. RESULTS Results: The metrics generally correlated with each other, but those that estimated the effect on clinical care correlated with the others the least and were generally associated with their own independent factors. The other metrics appeared to separate into those that emphasized errors in low glucose versus errors in high glucose. The Parkes grid was well correlated with the transformed glucose but not the estimation of clinical care. CONCLUSIONS Discussion: Our results indicate that we need to be careful before we assume that commonly used metrics like RMS error in raw glucose or even metrics like the Parkes grid that are designed to measure importance of differences will correlate well with actual effect on clinical care processes. A combination of metrics appeared to explain the most variance between cases. As prediction algorithms move into practice, it will be important to measure actual effects.

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