scholarly journals Data-Driven Approach to Improving the Risk Assessment Process of Medical Failures

2018 ◽  
Vol 15 (10) ◽  
pp. 2069 ◽  
Author(s):  
Shih-Heng Yu ◽  
Emily Su ◽  
Yi-Tui Chen
Keyword(s):  
Risk Assessment ◽  
Traditional Method ◽  
Failure Modes ◽  
Medical Center ◽  
Assessment Process ◽  
Data Driven ◽  
Relative Advantage ◽  
Data Envelopment ◽  
Data Driven Approach ◽  
Using Data

In recent decades, many researchers have focused on the issue of medical failures in the healthcare industry. A variety of techniques have been employed to assess the risk of medical failure and to generate strategies to reduce the frequency of medical failures. Considering the limitations of the traditional method—failure mode and effects analysis (FMEA)—for risk assessment and quality improvement, this paper presents two models developed using data envelopment analysis (DEA). One is called the slacks-based measure DEA (SBM-DEA) model, and the other is a novel data-driven approach (NDA) that combines FMEA and DEA. The relative advantages of the three models are compared. In this paper, an infant security case consisting of 16 failure modes at Western Wake Medical Center in Raleigh, North Carolina, U.S., was employed. The results indicate that both SBM-DEA and NDA may improve the discrimination and accuracy of detection compared to the traditional method of FMEA. However, NDA was found to have a relative advantage over SBM-DEA due to its risk assessment capability and precise detection of medical failures.

scholarly journals A Data-Driven Approach to Cyber Risk Assessment

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Paolo Santini ◽  
Giuseppe Gottardi ◽  
Marco Baldi ◽  
Franco Chiaraluce
Keyword(s):  
Risk Assessment ◽  
Quantitative Data ◽  
Economic Loss ◽  
Assessment Process ◽  
Risk Assessments ◽  
Data Driven ◽  
Quantitative Methodology ◽  
Data Driven Approach ◽  
Cyber Risk ◽  
A Company

Cyber risk assessment requires defined and objective methodologies; otherwise, its results cannot be considered reliable. The lack of quantitative data can be dangerous: if the assessment is entirely qualitative, subjectivity will loom large in the process. Too much subjectivity in the risk assessment process can weaken the credibility of the assessment results and compromise risk management programs. On the other hand, obtaining a sufficiently large amount of quantitative data allowing reliable extrapolations and previsions is often hard or even unfeasible. In this paper, we propose and study a quantitative methodology to assess a potential annualized economic loss risk of a company. In particular, our approach only relies on aggregated empirical data, which can be obtained from several sources. We also describe how the method can be applied to real companies, in order to customize the initial data and obtain reliable and specific risk assessments.

scholarly journals Generalized Read-Across prediction using genra-py

2021 ◽  
Author(s):  
Imran Shah ◽  
Tia Tate ◽  
Grace Patlewicz
Keyword(s):  
Risk Assessment ◽  
Human Health Risk ◽  
Chemical Properties ◽  
Predictive Performance ◽  
Hazard Identification ◽  
Data Driven ◽  
Chemical Safety ◽  
Physico Chemical ◽  
Data Driven Approach ◽  
Assessment Problems

Abstract Motivation Generalized Read-Across (GenRA) is a data-driven approach to estimate physico-chemical, biological or eco-toxicological properties of chemicals by inference from analogues. GenRA attempts to mimic a human expert’s manual read-across reasoning for filling data gaps about new chemicals from known chemicals with an interpretable and automated approach based on nearest-neighbors. A key objective of GenRA is to systematically explore different choices of input data selection and neighborhood definition to objectively evaluate predictive performance of automated read-across estimates of chemical properties. Results We have implemented genra-py as a python package that can be freely used for chemical safety analysis and risk assessment applications. Automated read-across prediction in genra-py conforms to the scikit-learn machine learning library's estimator design pattern, making it easy to use and integrate in computational pipelines. We demonstrate the data-driven application of genra-py to address two key human health risk assessment problems namely: hazard identification and point of departure estimation. Availability and implementation The package is available from github.com/i-shah/genra-py.

Modeling the Gas Exchange Processes of a Modern Diesel Engine With an Integrated Physics-Based and Data-Driven Approach

2019 ◽  
Author(s):  
Jorge Pulpeiro Gonzalez ◽  
King Ankobea-Ansah ◽  
Elena Escuder Milian ◽  
Carrie M. Hall
Keyword(s):  
Diesel Engine ◽  
Gas Exchange ◽  
High Efficiency ◽  
Direct Injection ◽  
Data Driven ◽  
Model Framework ◽  
Variable Valve Actuation ◽  
Exchange Processes ◽  
Data Driven Approach ◽  
Using Data

Abstract The gas exchange processes of engines are becoming increasingly complex since modern engines leverage technologies including variable valve actuation, turbochargers, and exhaust gas recirculation. Control of these many devices and the underlying gas flows is essential for high efficiency engine concepts. If these processes are to be controlled and estimated using model-based techniques, accurate models are required. This work explores a model framework that leverages a data-driven model of the turbocharger along with submodels of the intercooler, intake and exhaust manifolds and engine processes to provide cylinder-specific predictions of the pressure and temperatures of the gases across the system. This model is developed and validated using data from a 2.0 liter VW turbocharged, direct-injection diesel engine and shown to provide accurate prediction of critical gas properties.

Using data-driven approach for wind power prediction: A comparative study

2016 ◽  
Vol 118 ◽  
pp. 193-203 ◽  
Author(s):  
Ehsan Taslimi Renani ◽  
Mohamad Fathi Mohamad Elias ◽  
Nasrudin Abd. Rahim
Keyword(s):  
Comparative Study ◽  
Wind Power ◽  
Data Driven ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Data Driven Approach ◽  
Using Data

Prognostics of Embedded Planar Capacitors Under Temperature and Voltage Aging

2010 ◽  
Author(s):  
Mohammed A. Alam ◽  
Michael H. Azarian ◽  
Michael Osterman ◽  
Michael Pecht
Keyword(s):  
Elevated Temperature ◽  
Mahalanobis Distance ◽  
Failure Modes ◽  
Gradual Decrease ◽  
Data Driven ◽  
Time To Failure ◽  
Insulation Resistance ◽  
Sharp Drop ◽  
Model Based ◽  
Data Driven Approach

This paper presents the application of model-based and data-driven approaches for prognostics and health management (PHM) of embedded planar capacitors under elevated temperature and voltage conditions. An embedded planar capacitor is a thin laminate that serves both as a power/ground plane and as a parallel plate capacitor in a multilayered printed wiring board (PWB). These capacitors are typically used for decoupling applications and are found to reduce the required number of surface mount capacitors. The capacitor laminate used in this study consisted of an epoxy-barium titanate (BaTiO3) composite dielectric sandwiched between Cu layers. Three electrical parameters, capacitance, dissipation factor, and insulation resistance, were monitored in-situ once every hour during testing under elevated temperature and voltage aging conditions. The failure modes observed were a sharp drop in insulation resistance and a gradual decrease in capacitance. An approach to model the time-to-failure associated with these failure modes as a function of the stress level is presented in this paper. Model-based PHM can be used to predict the time-to-failure associated with a single failure mode, consisting of a drop in either insulation resistance or capacitance. However, failure of an embedded capacitor could occur due to either of these two failure modes and was not captured using a single model. A combined model for both these failure modes can be developed but there was a large variance in the time-to-failure data of failures as a result of a sharp drop in insulation resistance. Therefore a data-driven approach, which utilizes the trend and correlation between the parameters to predict remaining life, was investigated to perform PHM. The data-driven approach used in this paper is the Mahalanobis distance (MD) method that reduces a multivariate data set to a single parameter by considering correlations among the parameters. The Mahalanobis distance method was successful in predicting the failures as a result of a gradual decrease in capacitance. However, prediction of failures as a result of a drop in insulation resistance was generally challenging due to their sudden onset. An experimental approach to address such sudden failures is discussed to facilitate identifying any trends in the parameters prior to failure.

The Vanderbilt PICC Service: Program, Procedural, and Patient Outcomes Successes

2005 ◽  
Vol 10 (4) ◽  
pp. 183-192 ◽  
Author(s):  
Doug Burns
Keyword(s):  
Patient Outcomes ◽  
Medical Center ◽  
Nursing Program ◽  
Data Driven ◽  
Complication Rates ◽  
Advanced Practice ◽  
The Past ◽  
Data Driven Approach ◽  
High Level ◽  
Vanderbilt University

Abstract Since its inception in early 2000, Vanderbilt University's Peripherally Inserted Central Catheter (PICC) Service has experienced a high level of success as measured by high proficiency rates and increasing patient procedures each year, low complication rates during and after PICC placements, and an increasing scope of influence within the Vanderbilt University Medical Center and Children's Hospital, the surrounding community, and in the Southeastern United States. Primary drivers of the PICC Service's continuing success include consistent applications of technique and technology, a data-driven approach to assessing the program's progress, and appropriately managing customers' expectations and needs. Over the past five years, data were collected on more than 12,500 PICC placements performed in this specialized nursing program. Retrospective analyses of the data demonstrate an increasing rate of successful placements (from 87.2% to 92.4%) since the program's inception in 2000 to late 2004. Furthermore, the choice of PICC technology has had a significant impact on the odds for occlusion or infection. The Vanderbilt PICC Service provides a model by which other programs can be established, maintained, and expanded into advanced practice.

scholarly journals Risk Assessment of Civil Aircraft during Operation

2020 ◽  
Author(s):  
Longbiao Li
Keyword(s):  
Risk Assessment ◽  
Failure Modes ◽  
System Structure ◽  
Assessment Process ◽  
Risk Level ◽  
Failure Risk ◽  
System Risk ◽  
Hazard Level ◽  
Aircraft System

In this chapter, the risk assessment methods for aircraft system, structure, and aeroengine are investigated. For the aircraft system risk assessment, the probability level is divided into probable, improbable, and extremely improbable, and the hazard level of the failure condition is divided into minor, major, and catastrophic. Using Weibull analysis and Bayesian method to analyze the aircraft operation data, the risk level of aircraft system can be determined by combing methods provided in AC 25.1309-1A. For the aircraft structure risk assessment, the probability fracture mechanics approach can be used to determine the structure failure risk based on the data of material properties, environment, inspection, and so on. For the aeroengine risk assessment, the methods for classification of failure risk level, determination of hazard ratio, and calculation of the risk factor and risk per flight are given. The risk assessment process for aeroengine multi-failure modes based on the Monte Carlo simulation is presented to predict the occurrence of the failure and assess the failure risk.

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