Clustering and visualization of failure modes using an evolving tree

Despite the popularity of Failure Mode and Effect Analysis (FMEA) in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. As such, the idea of clustering and visualization pertaining to the failure modes in FMEA is proposed in this paper. A neural network visualization model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes in FMEA to be clustered and visualized as a tree structure. In addition, the ideas of risk interval and risk ordering for different groups of failure modes are proposed to allow the failure modes to be ordered, analyzed, and evaluated in groups. The main advantages of the proposed method lie in its ability to transform failure modes in a complex FMEA worksheet to a tree structure for better visualization, while maintaining the risk evaluation and ordering features. It can be applied to the conventional FMEA methodology without requiring additional information or data. A real world case study in the edible bird nest industry in Sarawak (Borneo Island) is used to evaluate the usefulness of the proposed method. The experiments show that the failure modes in FMEA can be effectively visualized through the tree structure. A discussion with FMEA users engaged in the case study indicates that such visualization is helpful in comprehending and analyzing the respective failure modes, as compared with those in an FMEA table. The resulting tree structure, together with risk interval and risk ordering, provides a quick and easily understandable framework to elucidate important information from complex FMEA forms; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is twofold, viz., the use of a computational visualization approach to tackling two well-known shortcomings of FMEA; and the use of ETree as an effective neural network learning paradigm to facilitate FMEA implementations. These findings aim to spearhead the potential adoption of FMEA as a useful and usable risk evaluation and management tool by the wider community. © 2015 Elsevier Ltd. All rights reserved

Untargeted metabolomics analysis of rat hippocampus subjected to sleep fragmentation

Sleep fragmentation (SF) commonly occurs in several pathologic conditions and is especially associated with impairments of hippocampus-dependent neurocognitive functions. Although the effects of SF on hippocampus in terms of protein or gene levels were examined in several studies, the impact of SF at the metabolite level has not been investigated. Thus, in this study, the differentially expressed large-scale metabolite profiles of hippocampus in a rat model of SF were investigated using untargeted metabolomics approaches. Forty-eight rats were divided into the following 4 groups: 4-day SF group, 4-day exercise control (EC) group, 15-day SF group, and 15-day EC group (n = 12, each). SF was accomplished by forced exercise using a walking wheel system with 30-s on/90-s off cycles, and EC condition was set at 10-min on/30-min off. The metabolite profiles of rat hippocampi in the SF and EC groups were analyzed using liquid chromatography/mass spectrometry. Multivariate analysis revealed distinctive metabolic profiles and marker signals between the SF and corresponding EC groups. Metabolic changes were significant only in the 15-day SF group. In the 15-day SF group, L-tryptophan, myristoylcarnitine, and palmitoylcarnitine were significantly increased, while adenosine monophosphate, hypoxanthine, L-glutamate, L-aspartate, L-methionine, and glycerophosphocholine were decreased compared to the EC group. The alanine, aspartate, and glutamate metabolism pathway was observed as the common key pathway in the 15-day SF groups. The results from this untargeted metabolomics study provide a perspective on metabolic impact of SF on the hippocampus.Peer reviewe

A Pilot Clinical Study in Characterization of Malignant Renal-cell Carcinoma Subtype with Contrast-enhanced Ultrasound

Malignant renal cell carcinoma (RCC) is a diverse set of diseases, which are independently difficult to characterize using conventional MRI and CT protocols due to low temporal resolution to study perfusion characteristics. Because different disease subtypes have different prognoses and involve varying treatment regimens, the ability to determine RCC subtype non-invasively is a clinical need. Contrast-enhanced ultrasound (CEUS) has been assessed as a tool to characterize kidney lesions based on qualitative and quantitative assessment of perfusion patterns, and we hypothesize that this technique might help differentiate disease subtypes. Twelve patients with RCC confirmed pathologically were imaged using contrast-enhanced ultrasound. Time intensity curves were generated and analyzed quantitatively using ten characteristic metrics. Results showed that peak intensity (p=0.001) and time-to-80% on wash-out (p=0.004) provided significant differences between clear cell, papillary, and chromophobe RCC subtypes. These results suggest that CEUS may be a feasible test for characterizing RCC subtypes

Quantifying morbidities by Adjusted Clinical Group system for a Taiwan population: A nationwide analysis

<p>Abstract</p> <p>Background</p> <p>The Adjusted Clinical Group (ACG) system has been used in measuring an individual's and a population's morbidities. Although all required inputs for running the ACG system are readily available, patients' morbidities and their associations to health care utilizations have been rarely studied in Taiwan. Therefore, the objective of this study was using the ACG system to quantify morbidities for Taiwanese population and to examine their relationship to ambulatory utilizations and costs.</p> <p>Methods</p> <p>This secondary analysis examined claims data for ambulatory services provided to 2.71 million representative Taiwanese in 2002 and 2003. People were grouped by the ACG system according to age, gender, and all ambulatory diagnosis codes in a given year. The software collapses the full set of ACGs into six morbidity categories (Non-users, Healthy, Low-morbidity, Moderate-, High- and Very-high) termed Resource Utilization Bands (RUBs). Each ACG was assigned a relative weight (RW), which was calculated as the ratio of mean ambulatory cost for each ACG to that for the overall. The distribution of morbidities was compared between years 2002 and 2003. The consistency of the distributions of visits, costs, and RWs of each ACG were examined for a two-year period. The relationship between people's morbidities and their ambulatory utilizations and costs was assessed.</p> <p>Results</p> <p>Ninety-eight percent of the subjects were correctly assigned to ACGs. Except for non-users (7.9 ~ 8.3%), most subjects were assigned to ACGs of acute and minor diseases and ACGs of moderate-to-high-morbid chronic diseases. The distributions of ACG-based morbidities were highly consistent (r = 0.949, <it>p < 0.001</it>) between 2002 and 2003. The ACG-specific visits (r = 0.955, <it>p < 0.001</it>), costs (r = 0.966, <it>p < 0.001</it>) and RWs (r = 0.991, <it>p < 0.001</it>) were correlated across two years. People grouped to the high-morbid ACGs had more visits and costs than those grouped to the low-morbid ACGs. Forty-six percent of the total ambulatory costs were spent by eighteen percent of the population, who were grouped to the High- and Very-high-morbidity RUBs.</p> <p>Conclusion</p> <p>This study demonstrated the feasibility, validity, and reliability of using the ACG system to measure morbidities in a Taiwan population and to explain their associations with ambulatory utilizations and costs for the whole country.</p

Comparison of alternative risk adjustment measures for predictive modeling: high risk patient case finding using Taiwan's National Health Insurance claims

<p>Abstract</p> <p>Background</p> <p>Predictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.</p> <p>Methods</p> <p>A random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.</p> <p>Results</p> <p>Both diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.</p> <p>Conclusions</p> <p>Predicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling.</p

Diagnostic accuracy of contrast-enhanced ultrasound for characterization of kidney lesions in patients with and without chronic kidney disease

Abstract Background Patients with chronic kidney disease are at increased risk of cystic kidney disease that requires imaging monitoring in many cases. However, these same patients often have contraindications to contrast-enhanced computed tomography and magnetic resonance imaging. This study evaluates the accuracy of contrast-enhanced ultrasound (CEUS), which is safe for patients with chronic kidney disease, for the characterization of kidney lesions in patients with and without chronic kidney disease. Methods We performed CEUS on 44 patients, both with and without chronic kidney disease, with indeterminate or suspicious kidney lesions (both cystic and solid). Two masked radiologists categorized lesions using CEUS images according to contrast-enhanced ultrasound adapted criteria. CEUS designation was compared to histology or follow-up imaging in cases without available tissue in all patients and the subset with chronic kidney disease to determine sensitivity, specificity and overall accuracy. Results Across all patients, CEUS had a sensitivity of 96% (95% CI: 84%, 99%) and specificity of 50% (95% CI: 32%, 68%) for detecting malignancy. Among patients with chronic kidney disease, CEUS sensitivity was 90% (95% CI: 56%, 98%), and specificity was 55% (95% CI: 36%, 73%). Conclusions CEUS has high sensitivity for identifying malignancy of kidney lesions. However, because specificity is low, modifications to the classification scheme for contrast-enhanced ultrasound could be considered as a way to improve contrast-enhanced ultrasound specificity and thus overall performance. Due to its sensitivity, among patients with chronic kidney disease or other contrast contraindications, CEUS has potential as an imaging test to rule out malignancy. Trial registration This trial was registered in clinicaltrials.gov, NCT01751529

Enhancing an evolving tree-based text document visualization model with fuzzy c-Means clustering

An improved evolving model, i.e., Evolving Tree (ETree) with Fuzzy c-Means (FCM), is proposed for undertaking text document visualization problems in this study. ETree forms a hierarchical tree structure in which nodes (i.e., trunks) are allowed to grow and split into child nodes (i.e., leaves), and each node represents a cluster of documents. However, ETree adopts a relatively simple approach to split its nodes. Thus, FCM is adopted as an alternative to perform node splitting in ETree. An experimental study using articles from a flagship conference of Universiti Malaysia Sarawak (UNIMAS), i.e., Engineering Conference (ENCON), is conducted. The experimental results are analyzed and discussed, and the outcome shows that the proposed ETree-FCM model is effective for undertaking text document clustering and visualization problems

A New Evolving Tree for Text Document Clustering and Visualization

The Self-Organizing Map (SOM) is a popular neural network model for clustering and visualization problems. However, it suffers from two major limitations, viz., (1) it does not support online learning; and (2) the map size has to be pre-determined and this can potentially lead to many “trial-and-error” runs before arriving at an optimal map size. Thus, an evolving model, i.e., the Evolving Tree (ETree), is used as an alternative to the SOM for undertaking a text document clustering problem in this study. ETree forms a hierarchical (tree) structure in which nodes are allowed to grow, and each leaf node represents a cluster of documents. An experimental study using articles from a flagship conference of Universiti Malaysia Sarawak (UNIMAS), i.e., the Engineering Conference (ENCON), is conducted. The experimental results are analyzed and discussed, and the outcome shows a new application of ETree in text document clustering and visualization

Clustering and visualization of failure modes using an evolving tree

Despite the popularity of Failure Mode and Effect Analysis (FMEA) in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. As such, the idea of clustering and visualization pertaining to the failure modes in FMEA is proposed in this paper. A neural network visualization model with an incremental learning feature, i.e., the evolving tree (ETree), is adopted to allow the failure modes in FMEA to be clustered and visualized as a tree structure. In addition, the ideas of risk interval and risk ordering for different groups of failure modes are proposed to allow the failure modes to be ordered, analyzed, and evaluated in groups. The main advantages of the proposed method lie in its ability to transform failure modes in a complex FMEA worksheet to a tree structure for better visualization, while maintaining the risk evaluation and ordering features. It can be applied to the conventional FMEA methodology without requiring additional information or data. A real world case study in the edible bird nest industry in Sarawak (Borneo Island) is used to evaluate the usefulness of the proposed method. The experiments show that the failure modes in FMEA can be effectively visualized through the tree structure. A discussion with FMEA users engaged in the case study indicates that such visualization is helpful in comprehending and analyzing the respective failure modes, as compared with those in an FMEA table. The resulting tree structure, together with risk interval and risk ordering, provides a quick and easily understandable framework to elucidate important information from complex FMEA forms; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is twofold, viz., the use of a computational visualization approach to tackling two well-known shortcomings of FMEA; and the use of ETree as an effective neural network learning paradigm to facilitate FMEA implementations. These findings aim to spearhead the potential adoption of FMEA as a useful and usable risk evaluation and management tool by the wider community