scholarly journals Applications of Big Data Analytics within a Dynamic Simulation Modeling Platform to Inform Osteoarthritis Care in Alberta

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Behnam Sharif ◽  
Shelly Vik ◽  
Deborah A Marshall-Catlin
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
Administrative Data ◽  
Average Cost ◽  
Input Parameter ◽  
Service Planning ◽  
Data Cube ◽  
Multidimensional Data ◽  
Number Of Patients ◽  
Input Parameters

IntroductionOsteoarthritis (OA) is a leading cause of chronic disability. There is need to leverage administrative data to support OA policy analysis. Our objective was to develop and apply a multidimensional data cube as an input parameter repository using health administrative data to populate an OA simulation model. Objectives and ApproachHealth administrative data including practitioner claims, inpatient and ambulatory visits from 1994 to 2013 were integrated into a multidimensional data cube. OA cases were identified using validated algorithms, and followed through stages of care (primary, specialist, acute and post-operative). The cube provided rate calculations, duration and average cost for each stage of care across the model dimensions (age categories, sex, comorbidity status and geographic zones). The rates were then linked to the model as input parameters to simulate patient flow across the continuum of care. We used the model to predict direct costs across all dimensions from 2010 to 2035. ResultsUsing the model, total number of patients with OA in Alberta will increase from 312,000 in 2010 to 1.4 million in 2035. The average annual cost per OA patient also increases from $2,800 to $4,900, and the total cost increases from $450 million in 2010 to 2.2 billion in 2035. The majority of the patients were at earlier stages (non-surgical 78%, surgical 22%), with lower average cost (non-surgical $3,300 vs. surgical $16,400) in 2010. As new administrative data are being provided routinely, the data cube is capable of providing real-time updates for the input parameters of the model, which will aid in validation of the model results and improving the precision of projections. Conclusion/ImplicationsThe data cube has significantly improved our ability to manage and analyze administrative data within a simulation model to project the burden of OA in Alberta. The integrated model can be used as a real time decision-support tool to inform osteoarthritis service planning and variations in resource utilization.

scholarly journals A Bayesian approach to estimating the population prevalence of mood and anxiety disorders using multiple measures

2021 ◽  
Vol 30 ◽  
Author(s):  
Jordan Edwards ◽  
A. Demetri Pananos ◽  
Amardeep Thind ◽  
Saverio Stranges ◽  
Maria Chiu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Anxiety Disorders ◽  
Administrative Data ◽  
Population Based ◽  
Accurate Estimate ◽  
Service Planning ◽  
Prevalence Estimate ◽  
Population Prevalence ◽  
Mood And Anxiety Disorders ◽  
Prevalence Estimates

Abstract Aims There is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure. Methods We used data from the 2012 Canadian Community Health Survey – Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results. Results The combined prevalence mean was 8.6%, with a credible interval of 6.8–10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data. Conclusions Accurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data.

scholarly journals A Real-Time, Automatic, and Dynamic Scheduling and Control System for PET Patients Based on Wearable Sensors

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1104
Author(s):  
Shin-Yan Chiou ◽  
Kun-Ju Lin ◽  
Ya-Xin Dong
Keyword(s):  
Control System ◽  
Real Time ◽  
Human Error ◽  
Dynamic Scheduling ◽  
Wearable Sensors ◽  
Time Data ◽  
Scan Time ◽  
Number Of Patients ◽  
And Control ◽  
Automatic Scheduling

Positron emission tomography (PET) is one of the commonly used scanning techniques. Medical staff manually calculate the estimated scan time for each PET device. However, the number of PET scanning devices is small, the number of patients is large, and there are many changes including rescanning requirements, which makes it very error-prone, puts pressure on staff, and causes trouble for patients and their families. Although previous studies proposed algorithms for specific inspections, there is currently no research on improving the PET process. This paper proposes a real-time automatic scheduling and control system for PET patients with wearable sensors. The system can automatically schedule, estimate and instantly update the time of various tasks, and automatically allocate beds and announce schedule information in real time. We implemented this system, collected time data of 200 actual patients, and put these data into the implementation program for simulation and comparison. The average time difference between manual and automatic scheduling was 7.32 min, and it could reduce the average examination time of 82% of patients by 6.14 ± 4.61 min. This convinces us the system is correct and can improve time efficiency, while avoiding human error and staff pressure, and avoiding trouble for patients and their families.

scholarly journals System Level Simulation of Microgrid Power Electronic Systems

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 644
Author(s):  
Michal Frivaldsky ◽  
Jan Morgos ◽  
Michal Prazenica ◽  
Kristian Takacs
Keyword(s):  
Smart Grid ◽  
Simulation Model ◽  
Real Time ◽  
Power Converter ◽  
System Level ◽  
Time Behavior ◽  
Operational Parameters ◽  
System Level Simulation ◽  
Time Operation ◽  
The Impact

In this paper, we describe a procedure for designing an accurate simulation model using a price-wised linear approach referred to as the power semiconductor converters of a DC microgrid concept. Initially, the selection of topologies of individual power stage blocs are identified. Due to the requirements for verifying the accuracy of the simulation model, physical samples of power converters are realized with a power ratio of 1:10. The focus was on optimization of operational parameters such as real-time behavior (variable waveforms within a time domain), efficiency, and the voltage/current ripples. The approach was compared to real-time operation and efficiency performance was evaluated showing the accuracy and suitability of the presented approach. The results show the potential for developing complex smart grid simulation models, with a high level of accuracy, and thus the possibility to investigate various operational scenarios and the impact of power converter characteristics on the performance of a smart gird. Two possible operational scenarios of the proposed smart grid concept are evaluated and demonstrate that an accurate hardware-in-the-loop (HIL) system can be designed.

Integrative Modeling Platform for Design and Control of an Adaptive Wind Turbine Blade

2018 ◽  
Author(s):  
Hamid Khakpour Nejadkhaki ◽  
John F. Hall ◽  
Minghui Zheng ◽  
Teng Wu
Keyword(s):  
Simulation Model ◽  
Wind Turbine ◽  
Real Time ◽  
Turbine Blade ◽  
Design Tool ◽  
Real Time Control ◽  
Time Control ◽  
Partial Load ◽  
And Control

A platform for the engineering design, performance, and control of an adaptive wind turbine blade is presented. This environment includes a simulation model, integrative design tool, and control framework. The authors are currently developing a novel blade with an adaptive twist angle distribution (TAD). The TAD influences the aerodynamic loads and thus, system dynamics. The modeling platform facilitates the use of an integrative design tool that establishes the TAD in relation to wind speed. The outcome of this design enables the transformation of the TAD during operation. Still, a robust control method is required to realize the benefits of the adaptive TAD. Moreover, simulation of the TAD is computationally expensive. It also requires a unique approach for both partial and full-load operation. A framework is currently being developed to relate the TAD to the wind turbine and its components. Understanding the relationship between the TAD and the dynamic system is crucial in the establishment of real-time control. This capability is necessary to improve wind capture and reduce system loads. In the current state of development, the platform is capable of maximizing wind capture during partial-load operation. However, the control tasks related to Region 3 and load mitigation are more complex. Our framework will require high-fidelity modeling and reduced-order models that support real-time control. The paper outlines the components of this framework that is being developed. The proposed platform will facilitate expansion and the use of these required modeling techniques. A case study of a 20 kW system is presented based upon the partial-load operation. The study demonstrates how the platform is used to design and control the blade. A low-dimensional aerodynamic model characterizes the blade performance. This interacts with the simulation model to predict the power production. The design tool establishes actuator locations and stiffness properties required for the blade shape to achieve a range of TAD configurations. A supervisory control model is implemented and used to demonstrate how the simulation model blade performs in the case study.

scholarly journals CONCEPT OF ECONOMICAL‐MATHEMATICAL MODEL OF LAND MARKET

2007 ◽  
Vol 13 (4) ◽  
pp. 333-340
Author(s):  
Gintautas Šatkauskas
Keyword(s):  
Mathematical Model ◽  
Input Parameter ◽  
Market Price ◽  
Research Process ◽  
Land Market ◽  
Market Prices ◽  
Noise Background ◽  
Input Parameters ◽  
Main Components ◽  
Sufficient Precision

Input parameters, ie factors defining the market price of agricultural‐purpose land, are interrelated very often by means of non‐linear ties. Strength of these ties is rather different and this limits usefulness of information in the research process of land market prices. Influence of input parameter changes to the input parameters in case when there are rather substantial changes may be determined in someone direction with a sufficient precision, whereas in other directions with comparatively small changes of input parameters this influence is difficult to be separated from the “noise” background. Taking into account the above‐listed circumstances, the concept of economical‐mathematical model of land market should be as follows: there is carried out re‐parameterisation of the process by means of introduction of new parameters in such a way that the new parameters are not interrelated, and the full process is evaluated at the minimal number of these parameters. These requirements are met by the main components of the input parameters. Then normalisation of the main components is carried out and dependencies on new parameters are determined. It is easier to interpret the dependencies obtained having reduced the number of input parameters and the higher the non‐linearity of interrelations of primary land market data, the greater effect of normalisation of input-parameter components. The results are compared with the valuations of experts.

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