scholarly journals Multi-Sensor Health Diagnosis Using Deep Belief Network Based State Classification

2011 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Pingfeng Wang ◽  
Byeng D. Youn
Keyword(s):  
Health State ◽  
Layer By Layer ◽  
Classification Models ◽  
Restricted Boltzmann Machines ◽  
Health States ◽  
Sensory Data ◽  
Complex Engineered Systems ◽  
State Classification ◽  
Health Diagnosis ◽  
Sensor Health

Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using Deep Belief Networks (DBN) based state classification. The DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked Restricted Boltzmann Machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using the DBN based state classification can be structured in three consecutive stages: first, defining health states and collecting sensory data for DBN training and testing; second, developing DBN based classification models for the diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. The performance of health diagnostics using DBN based health state classification is compared with four existing classification methods and demonstrated with two case studies.

A Hybrid Inference Approach for Health Diagnostics With Unexampled Faulty States

2012 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Pingfeng Wang
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Power Transformer ◽  
Roller Bearing ◽  
Health State ◽  
Health States ◽  
System Failures ◽  
State Classification ◽  
Testing Stage ◽  
Health Diagnosis

System health diagnostics provides diversified benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of engineered systems. Successful health diagnostics requires the knowledge of system failures. However, with an increasing complexity it is extraordinarily difficult to have a well-tested system so that all potential faulty states can be realized and studied at product testing stage. Thus, real time health diagnostics requires automatic detection of unexampled faulty states through the sensory signals to avoid sudden catastrophic system failures. This paper presents a hybrid inference approach (HIA) for structural health diagnosis with unexampled faulty states, which employs a two-fold inference process comprising of preliminary statistical learning based anomaly detection and artificial intelligence based health state classification for real time condition monitoring. The HIA is able to identify and isolate the unexampled faulty states through interactively detecting the deviation of sensory data from the known health states and forming new health states autonomously. The proposed approach takes the advantages of both statistical approaches and artificial intelligence based techniques and integrates them together in a unified diagnosis framework. The performance of proposed HIA is demonstrated with a power transformer and roller bearing health diagnosis case studies, where Mahalanobis distance serves as a representative statistical inference approach.

Methods for obtaining health state utility values: generic preference-based measures of health

2016 ◽  
Author(s):  
John Brazier ◽  
Julie Ratcliffe ◽  
Joshua A. Salomon ◽  
Aki Tsuchiya
Keyword(s):  
Health State Utility ◽  
Health State ◽  
Multiattribute Utility ◽  
Health States ◽  
Utility Values ◽  
Published Evidence ◽  
State Classification ◽  
Health State Utility Values ◽  
Generic Measures ◽  
Health State Classification

This chapter describes the six most widely used generic preference-based measures of health (GPBMs) (also known as multiattribute utility scales): EQ-5D, SF-6D, HUI, AQoL, 15D, and QWB. GPBMs have become the most widely used method for obtaining health state utility values. They contain a health state classification with multilevel dimensions that together describe a universe of health states and a set of values (where full health = 1 and dead = 0) for each health state obtained by eliciting the preferences (typically) of members of the general population. These measures are reviewed in terms of their content, methods of valuation, the scores they generate, and the possible reasons for the differences found. Their performance is reviewed using published evidence on their validity across conditions, and the implications for their use in policy making discussed. The chapter also reviews the generic measures available for use in populations of children and adolescents.

Estimating Utility Values for Health States of DFU Patients Using EQ-5D-5L and cTTO

2021 ◽  
pp. 153473462110143
Author(s):  
Morteza Arab-Zozani ◽  
Hossein Safari ◽  
Zoha Dori ◽  
Somayeh Afshari ◽  
Hosein Ameri ◽  
...  
Keyword(s):  
Foot Ulcer ◽  
Diabetic Foot Ulcer ◽  
Economic Modeling ◽  
Health State ◽  
Health States ◽  
Utility Values ◽  
The Mean ◽  
The Difference ◽  
Diabetes Center ◽  
Health State Utility Values

Health-state utility values of diabetic foot ulcer (DFU) patients are necessary for clinical praxis and economic modeling. The purpose of this study was to estimate utility values in DFU patients using the EuroQol-5-dimension-5-level (EQ-5D-5L) and composite time trade-off (cTTO). The EQ-5D-5L and cTTO were used for estimating utility values. Data were collected from 228 patients referred to the largest governmental diabetes center in the South of Iran, Yazd province. When appropriate, independent sample t-test or analysis of variance test was used to test the difference in the utility values in each of the demographic and clinical characteristics of the patients. Finally, the BetaMix was used to identify predictors of the utility values. The means of EQ-5D-5L and cTTO values were 0.55( SD 0.21) and 0.67( SD 0.23), respectively. Anxiety and pain were the most common problems reported by the patients. The difference between the mean EQ-5D-5L values was significant for age, grade of ulcer, number of comorbidities, and having complications. In addition, variables of gender, age, grade of ulcer, and having complications were significant predictors of the EQ-5D-5L. The difference between the mean cTTO values was significant for age, employment status, grade of ulcer, number of comorbidities, and having complications. Moreover, variables of gender, age, grade of ulcer, number of comorbidities, and developing complications were significant predictors of cTTO. The current study provided estimates of utility values for DFU patients for clinical praxis and economic modeling. These estimates, similar to utilities reported in other studies, were low. Identifying strategies to decrease anxiety/depression and pain in patients is important to improve the utility values.

scholarly journals Estimating an EQ-5D-3L Value Set for Romania Using Time Trade-Off

2021 ◽  
Vol 18 (14) ◽  
pp. 7415
Author(s):  
Marian Sorin Paveliu ◽  
Elena Olariu ◽  
Raluca Caplescu ◽  
Yemi Oluboyede ◽  
Ileana-Gabriela Niculescu-Aron ◽  
...  
Keyword(s):  
Health Technology Assessment ◽  
Technology Assessment ◽  
Health Technology ◽  
Assessment Process ◽  
Computer Assisted ◽  
Health State ◽  
Health States ◽  
Value Set ◽  
Health Technologies ◽  
Related Quality

Objective: To provide health-related quality of life (HRQoL) data to support health technology assessment (HTA) and reimbursement decisions in Romania, by developing a country-specific value set for the EQ-5D-3L questionnaire. Methods: We used the cTTO method to elicit health state values using a computer-assisted personal interviewing approach. Interviews were standardized following the most recent version of the EQ-VT protocol developed by the EuroQoL Foundation. Thirty EQ-5D-3L health states were randomly assigned to respondents in blocks of three. Econometric modeling was used to estimate values for all 243 states described by the EQ-5D-3L. Results: Data from 1556 non-institutionalized adults aged 18 years and older, selected from a national representative sample, were used to build the value set. All tested models were logically consistent; the final model chosen to generate the value set was an interval regression model. The predicted EQ-5D-3L values ranged from 0.969 to 0.399, and the relative importance of EQ-5D-3L dimensions was in the following order: mobility, pain/discomfort, self-care, anxiety/depression, and usual activities. Conclusions: These results can support reimbursement decisions and allow regional cross-country comparisons between health technologies. This study lays a stepping stone in the development of a health technology assessment process more driven by locally relevant data in Romania.

scholarly journals NCOG-36. FIRST HEALTH UTILITIES OF GLIOBLASTOMA PATIENTS USING TTFIELDS TREATMENT

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii137-ii137
Author(s):  
Gordon Chavez ◽  
Christina Proescholdt
Keyword(s):  
Population Sample ◽  
National Health System ◽  
Medical Decision ◽  
Economic Evaluations ◽  
Health State ◽  
High Quality ◽  
Health States ◽  
Tumor Treating Fields ◽  
Health State Utilities ◽  
The Uk

Abstract BACKGROUND Despite the importance of Health State Utilities for health policy and medical decision making, there are no publications that provide high quality utility values estimated from glioblastoma multiforme (GBM) patients. Published health economic evaluations for GBM treatments rely on utilities determined by Garside et al. (2007), which used the standard gamble method in healthy panel members of the UK National Health System. There are no published utilities for GBM estimated from a general population sample, and there are no utility estimates whatsoever for Tumor Treating Fields (TTFields) users. METHODS We designed a study to remedy this major deficit by eliciting utilities directly from GBM patients using the EuroQol 5-Dimension (EQ-5D) survey. The EQ-5D is a widely used and NICE-recommended tool for the estimation of health state utilities. The survey is composed of a questionnaire that asks patients to specify their health state along 5 dimensions: Mobility, Self-Care, Usual Activities, Pain/Discomfort, and Anxiety/Depression. Statistical models provided by EuroQol’s network of researchers convert this data into health state utility estimates. RESULTS The EQ-5D questionnaire is administered to active patients using TTFields treatment during the study duration, allowing the elicitation of health preference measures for different glioblastoma health states based on: progression status (progressed vs. non-progressed), current treatments (TTFields only vs. TTFields + others) and time-from-diagnosis (0-12 months vs. > 12 months) CONCLUSION These results are important for understanding the patient preferences using TTFields treatment and communicating these preferences to decision makers. This study is the first to provide direct, high quality utility measures in glioblastoma patients using TTFields treatment.

scholarly journals Valuation of the EQ-5D-3L in Russia

2021 ◽  
Author(s):  
Vitaly Omelyanovskiy ◽  
Nuriya Musina ◽  
Svetlana Ratushnyak ◽  
Tatiana Bezdenezhnykh ◽  
Vlada Fediaeva ◽  
...  
Keyword(s):  
Health Problems ◽  
Cost Utility ◽  
Economic Evaluations ◽  
Computer Assisted ◽  
Health State ◽  
Primary Analysis ◽  
Health States ◽  
Value Set ◽  
Model Combining ◽  
Life Years

Abstract Purpose The most widely used generic questionnaire to estimate the quality of life for yielding quality-adjusted life years in economic evaluations is EQ-5D. Country-specific population value sets are required to use EQ-5D in economic evaluations. The aim of this study was to establish an EQ-5D-3L value set for Russia. Methods A representative sample aged 18+ years was recruited from the Russia`s general population. Computer-assisted face–to–face interviews were conducted based on the standardized valuation protocol using EQ-Portable Valuation Technology. Population preferences were elicited utilizing both composite time trade-off (cTTO) and discrete choice experiment (DCE) techniques. To estimate the value set, a hybrid regression model combining cTTO and DCE data was used. Results A total of 300 respondents who successfully completed the interview were included in the primary analysis. 120 (40.0%) respondents reported no health problems of any dimension, and 56 (18.7%) reported moderate health problems in one dimension of the EQ‐5D‐3L. Median self-rated health using EQ‐VAS was 80 with IQR 70–90. Comparing cTTO and DCE-predicted values for 243 health states resulted in a similar pattern. This supports the use of hybrid models. The predicted value based on the preferred model for the worst health state “33333” was −0.503. Mobility dimension had the most significant impact on the utility decrement, and anxiety/depression had the lowest decrement. Conclusion Determining a Russian national value set may be considered the first step towards promoting cost-utility analysis use to increase comparability among studies and improve the transferability of healthcare decision-making in Russia.

scholarly journals Online Health Management for Complex Nonlinear Systems Based on Hidden Semi-Markov Model Using Sequential Monte Carlo Methods

2012 ◽  
Vol 2012 ◽  
pp. 1-22
Author(s):  
Qinming Liu ◽  
Ming Dong
Keyword(s):  
Monte Carlo ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Markov Model ◽  
Sequential Monte Carlo ◽  
Health Management ◽  
Health State ◽  
Health States ◽  
Complex Nonlinear System ◽  
Complex Nonlinear Systems

Health management for a complex nonlinear system is becoming more important for condition-based maintenance and minimizing the related risks and costs over its entire life. However, a complex nonlinear system often operates under dynamically operational and environmental conditions, and it subjects to high levels of uncertainty and unpredictability so that effective methods for online health management are still few now. This paper combines hidden semi-Markov model (HSMM) with sequential Monte Carlo (SMC) methods. HSMM is used to obtain the transition probabilities among health states and health state durations of a complex nonlinear system, while the SMC method is adopted to decrease the computational and space complexity, and describe the probability relationships between multiple health states and monitored observations of a complex nonlinear system. This paper proposes a novel method of multisteps ahead health recognition based on joint probability distribution for health management of a complex nonlinear system. Moreover, a new online health prognostic method is developed. A real case study is used to demonstrate the implementation and potential applications of the proposed methods for online health management of complex nonlinear systems.

scholarly journals On sensor optimisation for structural health monitoring robust to environmental variations

2021 ◽  
Vol 6 (5) ◽  
pp. 1107-1116
Author(s):  
Tingna Wang ◽  
David J. Wagg ◽  
Keith Worden ◽  
Robert J. Barthorpe
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Health State ◽  
Damage Classification ◽  
Health States ◽  
Structural Health ◽  
Classification Technique ◽  
Statistical Pattern ◽  
Environmental Variations ◽  
Using Data

Abstract. Structural health monitoring (SHM) is often approached from a statistical pattern recognition or machine learning perspective with the aim of inferring the health state of a structure using data derived from a network of sensors placed upon it. In this paper, two SHM sensor placement optimisation (SPO) strategies that offer robustness to environmental effects are developed and evaluated. The two strategies both involve constructing an objective function (OF) based upon an established damage classification technique and an optimisation of sensor locations using a genetic algorithm (GA). The key difference between the two strategies explored here is in whether any sources of benign variation are deemed to be observable or not. The relative performances of both strategies are demonstrated using experimental data gathered from a glider wing tested in an environmental chamber, with the structure tested in different health states across a series of controlled temperatures.

scholarly journals The Online Elicitation of Personal Utility Functions (OPUF) tool: a new method for valuing health states

2022 ◽  
Vol 7 ◽  
pp. 14
Author(s):  
Paul Schneider ◽  
Ben van Hout ◽  
Marike Heisen ◽  
John Brazier ◽  
Nancy Devlin
Keyword(s):  
Utility Functions ◽  
Health State ◽  
Group Level ◽  
Health States ◽  
Online Tool ◽  
Value Set ◽  
Individual Level ◽  
Personal Utility ◽  
Value Sets ◽  
The Individual

Introduction Standard valuation methods, such as TTO and DCE are inefficient. They require data from hundreds if not thousands of participants to generate value sets. Here, we present the Online elicitation of Personal Utility Functions (OPUF) tool; a new type of online survey for valuing EQ-5D-5L health states using more efficient, compositional elicitation methods, which even allow estimating value sets on the individual level. The aims of this study are to report on the development of the tool, and to test the feasibility of using it to obtain individual-level value sets for the EQ-5D-5L. Methods We applied an iterative design approach to adapt the PUF method, previously developed by Devlin et al., for use as a standalone online tool. Five rounds of qualitative interviews, and one quantitative pre-pilot were conducted to get feedback on the different tasks. After each round, the tool was refined and re-evaluated. The final version was piloted in a sample of 50 participants from the UK. A demo of the EQ-5D-5L OPUF survey is available at: https://eq5d5l.me Results On average, it took participants about seven minutes to complete the OPUF Tool. Based on the responses, we were able to construct a personal EQ-5D-5L value set for each of the 50 participants. These value sets predicted a participants' choices in a discrete choice experiment with an accuracy of 80%. Overall, the results revealed that health state preferences vary considerably on the individual-level. Nevertheless, we were able to estimate a group-level value set for all 50 participants with reasonable precision. Discussion We successfully piloted the OPUF Tool and showed that it can be used to derive a group-level as well as personal value sets for the EQ-5D-5L. Although the development of the online tool is still in an early stage, there are multiple potential avenues for further research.

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