scholarly journals How Prediction Accuracy Can Affect the Decision-Making Process in Pavement Management System

2021 ◽  
Vol 6 (2) ◽  
pp. 28
Author(s):  
Seyed Amirhossein Hosseini ◽  
Omar Smadi
Keyword(s):  
Decision Making ◽  
Prediction Model ◽  
Prediction Models ◽  
Reduction Rate ◽  
Pavement Management ◽  
Decision Making Process ◽  
Pavement Management Systems ◽  
Maintenance And Rehabilitation ◽  
Predicted Values ◽  
The Cost

One of the most important components of pavement management systems is predicting the deterioration of the network through performance models. The accuracy of the prediction model is important for prioritizing maintenance action. This paper describes how the accuracy of prediction models can have an effect on the decision-making process in terms of the cost of maintenance and rehabilitation activities. The process is simulating the propagation of the error between the actual and predicted values of pavement performance indicators. Different rate of error (10%, 30%, 50%, 70%, and 90%) was added into the result of prediction models. The results showed a strong correlation between the prediction models’ accuracy and the cost of maintenance and rehabilitation activities. The cost of treatment (in millions of dollars) over 20 years for five different scenarios increased from ($54.07–$92.95), ($53.89–$155.48), and ($74.41–$107.77) for asphalt, composite, and concrete pavement types, respectively. Increasing the rate of error also contributed to the prediction model, resulting in a higher benefit reduction rate.

How Prediction Accuracy can Affect the Decision-Making Process in Pavement Management System

2020 ◽  
Author(s):  
Seyed Amirhossein Hosseini ◽  
Omar Smadi
Keyword(s):  
Decision Making ◽  
Prediction Model ◽  
Prediction Models ◽  
Reduction Rate ◽  
Pavement Management ◽  
Decision Making Process ◽  
Pavement Management Systems ◽  
Maintenance And Rehabilitation ◽  
Predicted Values ◽  
The Cost

One of the most important components of pavement management systems is predicting the deterioration of the network through performance models. The accuracy of the prediction model is important for prioritizing maintenance action. This paper describes how the accuracy of prediction models can have an effect on the decision-making process in terms of the cost of maintenance and rehabilitation activities. The process is simulating the propagation of the error between the actual and predicted values of pavement performance indicators. Different rate of error was added into the result of prediction models. The results showed a strong correlation between the prediction models’ accuracy and the cost of maintenance and rehabilitation activities. Also, increasing the rate of error contribution to the prediction model resulting in a higher benefit reduction rate.

scholarly journals Uniform criteria for total hip replacement surgery in patients with hip osteoarthritis: a decision tool to guide treatment decisions

2021 ◽  
Vol 33 (1) ◽  
Author(s):  
Femke Atsma ◽  
Olivier Molenkamp ◽  
Heinse Bouma ◽  
Stefan B Bolder ◽  
A Stef Groenewoud ◽  
...  
Keyword(s):  
Decision Making ◽  
Prediction Model ◽  
Internal Rotation ◽  
Hip Replacement ◽  
Prediction Models ◽  
Hip Osteoarthritis ◽  
Decision Making Process ◽  
Decision Tool ◽  
Replacement Surgery ◽  
Hip Replacement Surgery

Abstract Background Uniform criteria for performing hip replacement surgery in hip osteoarthritis patients are currently lacking. As a result, variation in surgery and inappropriateness of care may occur. The aim of this study was to develop a consensus-based decision tool to support the decision-making process for hip replacement surgery. Methods Patients with a diagnosis of unilateral or bilateral osteoarthritis were included. Consensus rounds with orthopedic surgeons were organized to blindly reassess medical files and to decide whether surgery is indicated or not, based on all available pre-treatment information. We compared the outcomes obtained from the blind reassessment by the consensus group with the actual treatment. Furthermore, prediction models were fitted on the reassessment outcome to identify which set of clinical parameters would be most predictive and uniformly shared in the decision to operate. Two prediction models were fitted, one model without radiologic outcomes and one model where radiologic outcomes were included. Results In total, 364 medical files of osteoarthritis patients were included and reassessed in the analyses. Key predictors in the prediction model without radiology were age, flexion, internal rotation and the Hip disability and Osteoarthritis Outcome Score–quality of life. The discriminative power was high (Area Under Receiver Operating Curve (AUC) = 0.86). Key predictors in the prediction model with radiology were age, internal rotation and Kellgren and Lawrence severity score (AUC = 0.94). Conclusion The study yielded a decision tool with uniform criteria for hip replacement surgery in osteoarthritis patients. The tool will guide the clinical decision-making process of physicians on whether to perform hip surgery and should be used together with information about patient preferences and social context.

scholarly journals Active cutaneous warming systems to prevent intraoperative hypothermia: a systematic review

2012 ◽  
Vol 20 (1) ◽  
pp. 183-191 ◽  
Author(s):  
Vanessa de Brito Poveda ◽  
Edson Zangiacomi Martinez ◽  
Cristina Maria Galvão
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Body Temperature ◽  
Cost Benefit ◽  
Controlled Trials ◽  
Decision Making Process ◽  
Circulating Water ◽  
Randomized Controlled ◽  
Intraoperative Hypothermia ◽  
The Cost

This study analyzed the evidence available in the literature concerning the effectiveness of different active cutaneous warming systems to prevent intraoperative hypothermia. This is a systematic review with primary studies found in the following databases: CINAHL, EMBASE, Cochrane Register of Controlled Trials and Medline. The sample comprised 23 randomized controlled trials. There is evidence in the literature indicating that the circulating water garment system is the most effective in maintaining patient body temperature. These results can support nurses in the decision-making process concerning the implementation of effective measures to maintain normothermia, though the decision of health services concerning which system to choose should also take into account its cost-benefit status given the cost related to the acquisition of such systems.

scholarly journals RISK OF OVERINVESTMENT IN MUNICIPALITIES

2017 ◽  
Vol 16 (3) ◽  
pp. 63-71 ◽  
Author(s):  
Łukasz Satoła
Keyword(s):  
Decision Making ◽  
Public Services ◽  
Financial Resources ◽  
Decision Making Process ◽  
Business Activity ◽  
Benefit Analysis ◽  
Resources Management ◽  
Public Resources ◽  
Cost And Benefit Analysis ◽  
The Cost

This article describes investment activities of self-government territorial units. Its aim is to present the importance of investments for the provision of public services by municipalities. The opinions of the respondents about the causes of excessive or misguided investments and the ways of reducing their scale were presented. Surveys were conducted in 2015 and the temporal scope of the analysis is 2009–2014. The importance of investments for the provision of public services, shaping the living conditions of inhabitants, and conducting business activity were described. Based on that, overinvestment was identified as a negative trend in public resources management. The most frequent causes of excessive investment are megalomania of the municipality authorities and their desire to gain the support of the inhabitants (voters). Another important aspect is the lack of sufficient social control in the decision-making process regarding investment tasks execution. It was also demonstrated that overinvestment is due to the purpose of spending financial resources, not to the relative amount of investment expenses. Among the actions preventing excessive or misguided investments, the cost and benefit analysis was indicated the most often. Using strategic planning tools is also beneficial for the effectiveness of investing in self-government.

scholarly journals Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making

2021 ◽  
Vol 11 ◽  
Author(s):  
Jin-feng Pan ◽  
Rui Su ◽  
Jian-zhou Cao ◽  
Zhen-ya Zhao ◽  
Da-wei Ren ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Prediction Model ◽  
Prostate Biopsy ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Elevated Serum ◽  
Training Group ◽  
Validation Group ◽  
Clinical Indicators

PurposeThe purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.MethodsWe retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.ResultsIn the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.ConclusionThe prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.

Pavement Management Systems: Past, Present, and Future

2003 ◽  
Vol 1853 (1) ◽  
pp. 65-71 ◽  
Author(s):  
Ram B. Kulkarni ◽  
Richard W. Miller
Keyword(s):  
Performance Prediction ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Systems ◽  
Seamless Integration ◽  
Positioning Systems ◽  
Pavement Management Systems ◽  
Transportation Organization ◽  
Pavement Performance Prediction

The progress made over the past three decades in the key elements of pavement management systems was evaluated, and the significant improvements expected over the next 10 years were projected. Eight specific elements of a pavement management system were addressed: functions, data collection and management, pavement performance prediction, economic analysis, priority evaluation, optimization, institutional issues, and information technology. Among the significant improvements expected in pavement management systems in the next decade are improved linkage among, and better access to, databases; systematic updating of pavement performance prediction models by using data from ongoing pavement condition surveys; seamless integration of the multiple management systems of interest to a transportation organization; greater use of geographic information and Global Positioning Systems; increasing use of imaging and scanning and automatic interpretation technologies; and extensive use of formal optimization methods to make the best use of limited resources.

scholarly journals Statistics and Artificial Intelligence-Based Pavement Performance and Remaining Service Life Prediction Models for Flexible and Composite Pavement Systems

2020 ◽  
Vol 2674 (10) ◽  
pp. 448-460
Author(s):  
Orhan Kaya ◽  
Halil Ceylan ◽  
Sunghwan Kim ◽  
Danny Waid ◽  
Brian P. Moore
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Decision ◽  
Performance Models ◽  
Remaining Service Life ◽  
Management Decision Making ◽  
Decision Making Processes

In their pavement management decision-making processes, U.S. state highway agencies are required to develop performance-based approaches by the Moving Ahead for Progress in the 21st Century (MAP-21) federal transportation legislation. One of the performance-based approaches to facilitate pavement management decision-making processes is the use of remaining service life (RSL) models. In this study, a detailed step-by-step methodology for the development of pavement performance and RSL prediction models for flexible and composite (asphalt concrete [AC] over jointed plain concrete pavement [JPCP]) pavement systems in Iowa is described. To develop such RSL models, pavement performance models based on statistics and artificial intelligence (AI) techniques were initially developed. While statistically defined pavement performance models were found to be accurate in predicting pavement performance at project level, AI-based pavement performance models were found to be successful in predicting pavement performance in network level analysis. Network level pavement performance models using both statistics and AI-based approaches were also developed to evaluate the relative success of these two models for network level pavement performance modeling. As part of this study, in the development of pavement RSL prediction models, automation tools for future pavement performance predictions were developed and used along with the threshold limits for various pavement performance indicators specified by the Federal Highway Administration. These RSL models will help engineers in decision-making processes at both network and project levels and for different types of pavement management business decisions.

Incorporating bicycling demand into pavement management systems for convenient bikeway networks

2020 ◽  
Vol 47 (6) ◽  
pp. 759-770
Author(s):  
Feras Elsaid ◽  
Luis Amador-Jimenez ◽  
Ciprian Alecsandru
Keyword(s):  
Pavement Management ◽  
Management Systems ◽  
Pavement Condition ◽  
Pavement Management Systems ◽  
Strategic Plans ◽  
Maintenance And Rehabilitation ◽  
The World ◽  
Gps Trajectories

Several cities around the world have announced strategies to extend and (or) upgrade their bikeway networks in response to the rapid increase of bicycle users. However, there is a disconnection between these strategies and management systems, often used for the scheduling of maintenance and rehabilitation of roads. Traditional pavement management systems fail to incorporate bicycle pathways considering bicycling demand, along with pavement condition, as a driving element to budget for improvements. More convenient and safer bicycling facilities can encourage more individuals to shift their daily commuting habits to bicycling. In this study, we incorporate bicycling demand into pavement management systems to produce strategic plans for the maintenance and improvement of the bicycle networks. Furthermore, here we employ smartphones to represent bicycling demand using GPS trajectories of bicycles. In addition, goal optimization is applied to schedule interventions and improvements. Two scenarios are investigated with different annual budgets.

scholarly journals Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Lukas Falat ◽  
Dusan Marcek ◽  
Maria Durisova
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Decision Making ◽  
Soft Computing ◽  
Clustering Algorithm ◽  
Prediction Models ◽  
Moving Average ◽  
Series Data ◽  
Decision Making Process ◽  
Hybrid Neural Network

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined withK-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

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