A New Medical Device Modeling Framework for Predicting the Performance of Indwelling Continence Care Devices and Improving Patient Care

2021 ◽  
Author(s):  
Jeffrey Bodner ◽  
Walt Baxter ◽  
Christina Leung ◽  
Phillip Falkner
Keyword(s):  
Medical Device ◽  
Computational Models ◽  
Human Anatomy ◽  
Device Performance ◽  
Modeling Framework ◽  
Tissue Properties ◽  
Lack Of Information ◽  
Improving Patient Care ◽  
Medical Device Modeling ◽  
Basic Evaluation

Abstract Computational models that incorporate human anatomy, tissue biomechanics, and experimental measurements from animals or cadavers to predict medical device performance have proven useful. Since implant choices made by clinicians and biological tissue properties can vary widely across patients, these models tend to suffer from a fundamental lack of information about such variations that impact the analysis. To demonstrate a new means of overcoming such paucity of input data, the authors focused on a tractable device concern (that of temporary continence care lead movement) and allowed input properties to vary within the bounds of experiment to generate many simulations that ultimately predicted device performance. The computational model results were then compared with experimental results to build confidence in the predictions. The results suggest that a new method considering intervals of poorly defined and highly variable biomechanical and structural modeling inputs can faithfully predict device mechanics as measured in a cadaver model. Moreover, both model and experiment suggest that a new basic evaluation lead can provide more reliable fixation compared to the predicate device.

INFLUENCE OF PERSONAL COMMUNICATION NETWORKS ON INNOVATION ADOPTION — USING MULTI-AGENT SIMULATIONS TO PROJECT THE LAUNCH OF AN INNOVATIVE MEDICAL DEVICE

2013 ◽  
Vol 10 (05) ◽  
pp. 1340021 ◽  
Author(s):  
CHRISTIAN BARROT ◽  
JAN KUHLMANN ◽  
ANDREA POPA
Keyword(s):  
Medical Device ◽  
Interpersonal Communication ◽  
Communication Networks ◽  
Personal Communication ◽  
Market Penetration ◽  
Modeling Framework ◽  
Communication Processes ◽  
The Social ◽  
The Us ◽  
Multi Agent

Adoption processes are often heavily influenced by interpersonal communication. Marketing managers are increasingly trying to use these relationships to foster the market penetration of their products. In an empirical study of the US market for an innovative medical device, we survey the social network of (mostly chief) anesthetists from 151 hospitals. We confirm the influences from personal communication on individual adoption decisions through hazard regressions. We then use a multi-agent modeling framework trying to identify what seeding strategies would have been optimal to achieve a fast market penetration, i.e. which and how many anesthetists should be selected to initiate personal communication processes.

scholarly journals Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model

2020 ◽  
Vol 378 (2173) ◽  
pp. 20190347 ◽  
Author(s):  
Nick van Osta ◽  
Aurore Lyon ◽  
Feddo Kirkels ◽  
Tijmen Koopsen ◽  
Tim van Loon ◽  
...  
Keyword(s):  
Right Ventricular ◽  
Computational Models ◽  
Clinical Decision Making ◽  
Early Stage ◽  
Patient Specific ◽  
Model Parameters ◽  
Arrhythmogenic Cardiomyopathy ◽  
Tissue Properties ◽  
Myocardial Disease ◽  
Deformation Patterns

Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Patient-specific computational models could help understand the disease progression and may help in clinical decision-making. We propose an inverse modelling approach using the CircAdapt model to estimate patient-specific regional abnormalities in tissue properties in AC subjects. However, the number of parameters ( n  = 110) and their complex interactions make personalized parameter estimation challenging. The goal of this study is to develop a framework for parameter reduction and estimation combining Morris screening, quasi-Monte Carlo (qMC) simulations and particle swarm optimization (PSO). This framework identifies the best subset of tissue properties based on clinical measurements allowing patient-specific identification of right ventricular tissue abnormalities. We applied this framework on 15 AC genotype-positive subjects with varying degrees of myocardial disease. Cohort studies have shown that atypical regional right ventricular (RV) deformation patterns reveal an early-stage AC disease. The CircAdapt model of cardiovascular mechanics and haemodynamics has already demonstrated its ability to capture typical deformation patterns of AC subjects. We, therefore, use clinically measured cardiac deformation patterns to estimate model parameters describing myocardial disease substrates underlying these AC-related RV deformation abnormalities. Morris screening reduced the subset to 48 parameters. qMC and PSO further reduced the subset to a final selection of 16 parameters, including regional tissue contractility, passive stiffness, activation delay and wall reference area. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

scholarly journals Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization

Cells ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 881 ◽  
Author(s):  
Jihwan Ha ◽  
Chihyun Park ◽  
Chanyoung Park ◽  
Sanghyun Park
Keyword(s):  
Computational Models ◽  
Matrix Completion ◽  
Area Under The Curve ◽  
Excellent Performance ◽  
Novel Mirna ◽  
Lack Of Information ◽  
Disease Associations ◽  
Roc Area ◽  
Leave One Out ◽  
Global And Local

The identification of potential microRNA (miRNA)-disease associations enables the elucidation of the pathogenesis of complex human diseases owing to the crucial role of miRNAs in various biologic processes and it yields insights into novel prognostic markers. In the consideration of the time and costs involved in wet experiments, computational models for finding novel miRNA-disease associations would be a great alternative. However, computational models, to date, are biased towards known miRNA-disease associations; this is not suitable for rare miRNAs (i.e., miRNAs with a few known disease associations) and uncommon diseases (i.e., diseases with a few known miRNA associations). This leads to poor prediction accuracies. The most straightforward way of improving the performance is by increasing the number of known miRNA-disease associations. However, due to lack of information, increasing attention has been paid to developing computational models that can handle insufficient data via a technical approach. In this paper, we present a general framework—improved prediction of miRNA-disease associations (IMDN)—based on matrix completion with network regularization to discover potential disease-related miRNAs. The success of adopting matrix factorization is demonstrated by its excellent performance in recommender systems. This approach considers a miRNA network as additional implicit feedback and makes predictions for disease associations relevant to a given miRNA based on its direct neighbors. Our experimental results demonstrate that IMDN achieved excellent performance with reliable area under the receiver operating characteristic (ROC) area under the curve (AUC) values of 0.9162 and 0.8965 in the frameworks of global and local leave-one-out cross-validations (LOOCV), respectively. Further, case studies demonstrated that our method can not only validate true miRNA-disease associations but also suggest novel disease-related miRNA candidates.

scholarly journals A practical guide to mechanistic systems modeling in biology using a logic-based approach

2020 ◽  
Author(s):  
Anna Niarakis ◽  
Tomáš Helikar
Keyword(s):  
Computational Modeling ◽  
Computational Models ◽  
Regulatory System ◽  
Mechanistic Modeling ◽  
Lac Operon ◽  
Modeling Framework ◽  
Validation Criteria ◽  
Hands On ◽  
Modeling Software ◽  
Logical Modeling

Abstract Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process—the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h.

scholarly journals The Space Weather Modeling Framework Goes Open Access

Eos ◽  
2021 ◽  
Vol 102 ◽  
Author(s):  
Tuija Pulkkinen ◽  
Tamas Gombosi ◽  
Aaron Ridley ◽  
Gabor Toth ◽  
Shasha Zou
Keyword(s):  
Open Access ◽  
Space Weather ◽  
Computational Models ◽  
Power Grid ◽  
Magnetic Storms ◽  
Modeling Framework ◽  
Weather Modeling

A versatile suite of computational models, already used to forecast magnetic storms and potential power grid and telecommunications disruptions, is preparing to welcome a larger group of users.

scholarly journals Top-Down Contributions to Attention Shifting and Disengagement: A Template Model of Visual Attention

2018 ◽  
Author(s):  
Motonori Yamaguchi
Keyword(s):  
Visual Attention ◽  
Computational Models ◽  
Short Term Memory ◽  
Top Down ◽  
Short Term ◽  
Modeling Framework ◽  
Term Memory ◽  
Visual Item ◽  
Driven System ◽  
Fixation Offset

Two separate systems are involved in the control of spatial attention; one that is driven by a goal, and the other that is driven by stimuli. While the goal- and stimulus-driven systems follow different general principles, they also interplay with each other. However, the mechanism by which the goal-driven system influences the stimulus-driven system is still debated. The present study examined top-down contributions to two components of attention orienting, shifting and disengagement, with an experimental paradigm in which participants held a visual item in short-term memory and performed a prosaccade task with a manipulation of the gap between fixation offset and target onset. Four experiments showed that the short-term memory content accelerated shifting and impaired disengagement, but the influence on disengagement depended on the utility of short-term memory in guiding attention toward the target. Thus, the use of short-term memory was strategic. Computational models of visual attention were fitted to the experimental data, which suggested that the top-down contributions to shifting was more prominent than those to disengagement. The present study shows that the current modeling framework was particularly useful when examining the contributions of theoretical constructs for the control of visual attention, but it also suggests limitations.

Prediction of medical device performance using machine learning techniques: infant incubator case study

2019 ◽  
Vol 10 (1) ◽  
pp. 151-155 ◽  
Author(s):  
Živorad Kovačević ◽  
Lejla Gurbeta Pokvić ◽  
Lemana Spahić ◽  
Almir Badnjević
Keyword(s):  
Machine Learning ◽  
Medical Device ◽  
Machine Learning Techniques ◽  
Device Performance ◽  
Learning Techniques ◽  
Infant Incubator

scholarly journals The Model of Good Health

2018 ◽  
Vol 140 (04) ◽  
pp. 32-37
Author(s):  
Jean Thilmany
Keyword(s):  
Medical Device ◽  
Good Health ◽  
Human Anatomy ◽  
Analysis Software ◽  
Building Model ◽  
Modeling Simulation ◽  
Regulatory Testing ◽  
3D Printers ◽  
Radiological Health ◽  
Design And Testing

This article explores the innovative ways of using advanced modeling, simulation, and analysis software in medical field. In order to meet design requirements, engineers who work for medical device makers have been putting advanced modeling, simulation, and analysis software to use in innovative ways, such as creating models of the human anatomy that can be used to virtually test potential medical technologies. They have also put new tools such as 3D printers to work building model prototypes for real-world testing. The Food and Drug Administration’s Center for Devices and Radiological Health is now creating a simulated human capable of serving as an in-silico guinea pig. The center is building a library of computer regulatory testing models and a family of ‘virtual patients’ for product design and testing. The article also describes that medical device developers can use cinematic rendering, such as an image of the blood vessels in the skull created in Syngio via Frontier, an application enabling the realistic depiction of volume datasets, to help create better treatments.

Sign in / Sign up

Export Citation Format

Share Document