Roll eccentricity extraction and compensation based on MPSO-WTD and ITD.

To meet the high thickness accuracy requirements in cold-rolling processes, a roll eccentricity signal extraction method based on modified particle swarm optimization and wavelet threshold denoising (MPSO-WTD) with intrinsic time-scale decomposition (ITD) is proposed. The strong denoising ability of the wavelet is combined with the decomposition and recognition attributes of ITD for non-stationary signals. Periodic disturbances in strip thickness caused by roll eccentricity are actively compensated. First, the wavelet is used to denoise the signal and the MPSO algorithm is applied to determine a rational threshold and improve the calculation efficiency. Then, the denoised signal is decomposed into proper rotational components (PRCs) using the ITD method, and an appropriate PRC component representing the eccentricity signal is extracted. Finally, the eccentricity compensation signal is applied in the automatic gauge control (AGC) system of the cold rolling mill. During the rolling process, the rolling speed is not constant and will directly affect the frequency of the roll eccentricity signal. To solve this problem, an encoder is installed at the end of the roll and the compensation frequency of the roller eccentricity signal is determined in the roller eccentricity compensation system according to the pulse number output. The results of simulations and experiments show that roll eccentricity signals extracted using the proposed method can effectively remove the influence of interference signals. An average improvement of 62.3% in the roll eccentricity compensation effect was achieved under the stable rolling condition in the finishing rolling stage.

HARKing, Cherry-Picking, P-Hacking, Fishing Expeditions, and Data Dredging and Mining as Questionable Research Practices.

Questionable research practices (QRPs) in the statistical analysis of data and in the presentation of the results in research papers include HARKing, cherry-picking, P-hacking, fishing, and data dredging or mining. HARKing (Hypothesizing After the Results are Known) is the presentation of a post hoc hypothesis as an a priori hypothesis. Cherry-picking is the presentation of favorable evidence with the concealment of unfavorable evidence. P-hacking is the relentless analysis of data with an intent to obtain a statistically significant result, usually to support the researcher's hypothesis. A fishing expedition is the indiscriminate testing of associations between different combinations of variables not with specific hypotheses in mind but with the hope of finding something that is statistically significant in the data. Data dredging and data mining describe the extensive testing of relationships between a large number of variables for which data are available, usually in a database. This article explains what these QRPs are and why they are QRPs. This knowledge must become widespread so that researchers and readers understand what approaches to statistical analysis and reporting amount to scientific misconduct.

A prospective comparison of evidence synthesis search strategies developed with and without text-mining tools.

OBJECTIVE: We compared the process of developing searches with and without using text-mining tools (TMTs) for evidence synthesis products. STUDY DESIGN: This descriptive comparative analysis included seven systematic reviews, classified as simple or complex. Two librarians created MEDLINE strategies for each review, using either usual practice (UP) or TMTs. For each search we calculated sensitivity, number-needed-to-read (NNR) and time spent developing the search strategy. RESULTS: We found UP searches were more sensitive (UP 92% (95% CI, 85-99); TMT 84.9% (95% CI, 74.4-95.4)), with lower NNR (UP 83 (SD 34); TMT 90 (SD 68)). UP librarians spent an average of 12 h (SD 8) developing search strategies, compared to TMT librarians' 5 hours (SD 2). CONCLUSION: Across all reviews, TMT searches were less sensitive than UP searches, but confidence intervals overlapped. For simple SR topics, TMT searches were faster and slightly less sensitive than UP. For complex SR topics, TMT searches were faster and less sensitive than UP searches but identified unique eligible citations not found by the UP searches.

Analysis of motorcycle accidents using association rule mining-based framework with parameter optimization and GIS technology.

INTRODUCTION: Analyzing key factors of motorcycle accidents is an effective method to reduce fatalities and improve road safety. Association Rule Mining (ARM) is an efficient data mining method to identify critical factors associated with injury severity. However, the existing studies have some limitations in applying ARM: (a) Most studies determined parameter thresholds of ARM subjectively, which lacks objectiveness and efficiency; (b) Most studies only listed rules with high parameter thresholds, while lacking in-depth analysis of multiple-item rules. Besides, the existing studies seldom conducted a spatial analysis of motorcycle accidents, which can provide intuitive suggestions for policymakers. METHOD: To address these limitations, this study proposes an ARM-based framework to identify critical factors related to motorcycle injury severity. A method for parameter optimization is proposed to objectively determine parameter thresholds in ARM. A method of factor extraction is proposed to identify individual key factors from 2-item rules and boosting factors from multiple-item rules. Geographic information system (GIS) is adopted to explore the spatial relationship between key factors and motorcycle injury severity. RESULTS AND CONCLUSIONS: The framework is applied to a case study of motorcycle accidents in Victoria, Australia. Fifteen attributes are selected after data preprocessing. 0.03 and 0.7 are determined as the best thresholds of support and confidence in ARM. Five individual key factors and four boosting factors are identified to be related to fatal injury. Spatial analysis is conducted by GIS to present hot spots of motorcycle accidents. The proposed framework has been validated to have better performance on parameter optimization and rule analysis in ARM. Practical applications: The hot spots of motorcycle accidents related to fatal factors are presented in GIS maps. Policymakers can refer to those maps straightforwardly when decision making. This framework can be applied to various kinds of traffic accidents to improve the performance of severity analysis.

Ethical Issues and the Electronic Health Record.

Ethical issues related to electronic health records (EHRs) confront health personnel. Electronic health records create conflict among several ethical principals. Electronic health records may represent beneficence because they are alleged to increase access to health care, improve the quality of care and health, and decrease costs. Research, however, has not consistently demonstrated access for disadvantaged persons, the accuracy of EHRs, their positive effects on productivity, nor decreased costs. Should beneficence be universally acknowledged, conflicts exist with other ethical principles. Autonomy is jeopardized when patients' health data are shared or linked without the patients' knowledge. Fidelity is breached by the exposure of thousands of patients' health data through mistakes or theft. Lack of confidence in the security of health data may induce patients to conceal sensitive information. As a consequence, their treatment may be compromised. Justice is breached when persons, because of their socioeconomic class or age, do not have equal access to health information resources and public health services. Health personnel, leaders, and policy makers should discuss the ethical implications of EHRs before the occurrence of conflicts among the ethical principles. Recommendations to guide health personnel, leaders, and policy makers are provided.

Assessment of the Electronic Health Record Skills Needed for the Health Information Management Student.

Electronic health records (EHR) have continued to advance and improve patient care, treatment, and safety, but the education required for EHR use can vary. In preparing future health care professionals for the use of EHR, allied health programs such as health information management (HIM) should understand the current use of EHR skills of HIM professionals. This quantitative descriptive study identified the current use of EHR skills of HIM professionals within a region. An email containing a link to the electronic survey was sent to 350 HIM association members. The response rate was 34.6% (n=121). The results indicated higher use of federal and state regulations regarding privacy/security, problem solving and critical thinking skills for health information technology (HIT) systems, and data mining skills. But, there were some skillsets that had a lower use such as financial decision making, database design, and HIT software development. The findings suggest some specific EHR skills that are essential for HIM graduates. It is imperative that HIM programs have an understanding of what EHR skills are needed for their profession.

Anthropometric data quality assessment in multisurvey studies of child growth.

BACKGROUND: Population-based surveys collect crucial data on anthropometric measures to track trends in stunting [height-for-age z score (HAZ) < -2SD] and wasting [weight-for-height z score (WHZ) < -2SD] prevalence among young children globally. However, the quality of the anthropometric data varies between surveys, which may affect population-based estimates of malnutrition. OBJECTIVES: We aimed to develop composite indices of anthropometric data quality for use in multisurvey analysis of child health and nutritional status. METHODS: We used anthropometric data for children 0-59 mo of age from all publicly available Demographic and Health Surveys (DHS) from 2000 onwards. We derived 6 indicators of anthropometric data quality at the survey level, including 1) date of birth completeness, 2) anthropometric measure completeness, 3) digit preference for height and age, 4) difference in mean HAZ by month of birth, 5) proportion of biologically implausible values, and 6) dispersion of HAZ and WHZ distribution. Principal component factor analysis was used to generate a composite index of anthropometric data quality for HAZ and WHZ separately. Surveys were ranked from the highest (best) to the lowest (worst) index values in anthropometric quality across countries and over time. RESULTS: Of the 145 DHS included, the majority (83 of 145; 57%) were conducted in Sub-Saharan Africa. Surveys were ranked from highest to lowest anthropometric data quality relative to other surveys using the composite index for HAZ. Although slightly higher values in recent DHS suggest potential improvements in anthropometric data quality over time, there continues to be substantial heterogeneity in the quality of anthropometric data across surveys. Results were similar for the WHZ data quality index. CONCLUSIONS: A composite index of anthropometric data quality using a parsimonious set of individual indicators can effectively discriminate among surveys with excellent and poor data quality. Such indices can be used to account for variations in anthropometric data quality in multisurvey epidemiologic analyses of child health.

Two Decades of Research Using Taiwan's National Health Insurance Claims Data: Bibliometric and Text Mining Analysis on PubMed.

BACKGROUND: Studies using Taiwan's National Health Insurance (NHI) claims data have expanded rapidly both in quantity and quality during the first decade following the first study published in 2000. However, some of these studies were criticized for being merely data-dredging studies rather than hypothesis-driven. In addition, the use of claims data without the explicit authorization from individual patients has incurred litigation. OBJECTIVE: This study aimed to investigate whether the research output during the second decade after the release of the NHI claims database continues growing, to explore how the emergence of open access mega journals (OAMJs) and lawsuit against the use of this database affect the research topics and publication volume and to discuss the underlying reasons. METHODS: PubMed was used to locate publications based on NHI claims data between 1996 and 2017. Concept extraction using MetaMap was employed to mine research topics from article titles. Research trends were analyzed from various aspects, including publication amount, journals, research topics and types, and cooperation between authors. RESULTS: A total of 4473 articles were identified. A rapid growth in publications was witnessed from 2000 to 2015, followed by a plateau. Diabetes, stroke, and dementia were the top 3 most popular research topics whereas statin therapy, metformin, and Chinese herbal medicine were the most investigated interventions. Approximately one-third of the articles were published in open access journals. Studies with two or more medical conditions, but without any intervention, were the most common study type. Studies of this type tended to be contributed by prolific authors and published in OAMJs. CONCLUSIONS: The growth in publication volume during the second decade after the release of the NHI claims database was different from that during the first decade. OAMJs appeared to provide fertile soil for the rapid growth of research based on NHI claims data, in particular for those studies with two or medical conditions in the article title. A halt in the growth of publication volume was observed after the use of NHI claims data for research purposes had been restricted in response to legal controversy. More efforts are needed to improve the impact of knowledge gained from NHI claims data on medical decisions and policy making.

Data normalization strategies in metabolomics: Current challenges, approaches, and tools.

Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from - either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.

Features spaces and a learning system for structural-temporal data, and their application on a use case of real-time communication network validation data.

The service quality and system dependability of real-time communication networks strongly depends on the analysis of monitored data, to identify concrete problems and their causes. Many of these can be described by either their structural or temporal properties, or a combination of both. As current research is short of approaches sufficiently addressing both properties simultaneously, we propose a new feature space specifically suited for this task, which we analyze for its theoretical properties and its practical relevance. We evaluate its classification performance when used on real-world data sets of structural-temporal mobile communication data, and compare it to the performance achieved of feature representations used in related work. For this purpose we propose a system which allows the automatic detection and prediction of classes of pre-defined sequence behavior, greatly reducing costs caused by the otherwise required manual analysis. With our proposed feature spaces this system achieves a precision of more than 93% at recall values of 100%, with an up to 6.7% higher effective recall than otherwise similarly performing alternatives, notably outperforming alternative deep learning, kernel learning and ensemble learning approaches of related work. Furthermore the supported system calibration allows separating reliable from unreliable predictions more effectively, which is highly relevant for any practical application.

The Data Tags Suite (DATS) model for discovering data access and use requirements.

BACKGROUND: Data reuse is often controlled to protect the privacy of subjects and patients. Data discovery tools need ways to inform researchers about restrictions on data access and re-use. RESULTS: We present elements in the Data Tags Suite (DATS) metadata schema describing data access, data use conditions, and consent information. DATS metadata are explained in terms of the administrative, legal, and technical systems used to protect confidential data. CONCLUSIONS: The access and use metadata items in DATS are designed from the perspective of a researcher who wants to find and re-use existing data. We call for standard ways of describing informed consent and data use agreements that will enable automated systems for managing research data.

Data detectives, self-love, and humility: a research parasite's perspective.

Secondary analysis solidifies and expands upon scientific knowledge through the re-analysis of existing datasets. However, researchers performing secondary analyses must develop specific skills to be successful and can benefit from adopting some computational best practices. Recognizing this work is also key to building and supporting a community of researchers who contribute to the scientific ecosystem through secondary analyses. The Research Parasite Awards are one such avenue, celebrating outstanding contributions to the rigorous secondary analysis of data. As the recipient of a 2019 Junior Research Parasite Award, I was asked to provide some perspectives on life as a research parasite, which I share in this commentary.

Taxonomic Triangulation of Care in Healthcare Protocols: Mapping of Diagnostic Knowledge From Standardized Language.

Taxonomic triangulation is a data mining technique for the management of care knowledge. This technique uses standardized languages, such as North American Nursing Diagnosis Association International, Nursing Outcomes Classification, and Nursing Interventions Classification, as well as logic. Its purpose is to find patterns in the data and identify care diagnoses. Triangulation can be applied to databases (clinical records) or to bibliographic sources (eg, protocols). The objective of this study is to identify the care diagnoses implicit in the nursing care protocols of the Community of Madrid. The method followed has three phases: knowledge extraction for mapping of variables, linking to diagnoses, and triangulation with analysis. The study analyzes six protocols, and 344 variables (167 assessment, 29 planning, and 148 intervention) and 6118 links have been extracted. Triangulation identified 165 NANDA diagnoses (68.48%), and only 25 labels were not revealed through this process. As a limitation, the results depend on the knowledge presented in protocols and change with language editions. Some labels included in the sample are recent and are not included in the links with nursing outcomes classification and nursing interventions classification. In conclusion, taxonomic triangulation makes it possible to manage knowledge, discover data patterns, and represent care situations.

Optimization of Electronic Medical Records for Data Mining Using a Common Data Model.

The increasing use of electronic health records (EHRs) in veterinary medicine creates an opportunity to utilize the high volume of electronic patient data for mining and data-driven analytics with the goal of improving patient care and outcomes. A central focus of the Clinical and Translational Science Award One Health Alliance (COHA) is to integrate efforts across multiple disciplines to better understand shared diseases in animals and people. The ability to combine veterinary and human medical data provides a unique resource to study the interactions and relationships between animals, humans, and the environment. However, to effectively answer these questions, veterinary EHR data must first be prepared in the same way it is now commonly being done in human medicine to enable data mining and development of analytics to facilitate knowledge formation and solutions that advance our understanding of disease processes, with the ultimate goal of improving outcomes for veterinary patients and their owners. As a first step, COHA member institutions implemented a Common Data Model to standardize EHR data. Herein we present the approach executed within the COHA framework to prepare and optimize veterinary EHRs for data mining and knowledge formation based on the adoption of the Observational Health Data Sciences and Informatics' Observational Medical Outcomes Partnership Common Data Model.

Mining version history to predict the class instability.

While most of the existing class stability assessors just rely on structural information retrieved from a desired source code snapshot. However, class stability is intrinsically characterized by the evolution of a number of dependencies and change propagation factors which aid to promote the ripple effect. Identification of classes prone to ripple effect (instable classes) through mining the version history of change propagation factors can aid developers to reduce the efforts needed to maintain and evolve the system. We propose Historical Information for Class Stability Prediction (HICSP), an approach to exploit change history information to predict the instable classes based on its correlation with change propagation factors. Subsequently, we performed two empirical studies. In the first study, we evaluate the HICSP on the version history of 10 open source projects. Subsequently, in the second replicated study, we evaluate the effectiveness of HICSP by tuning the parameters of its stability assessors. We observed the 4 to 16 percent improvement in term of F-measure value to predict the instable classes through HICSP as compared to existing class stability assessors. The promising results indicate that HICSP is able to identify instable classes and can aid developers in their decision making.

Improving the combination of results in the ensembles of prototype selectors.

Prototype selection is one of the most common preprocessing tasks in data mining applications. The vast amounts of data that we must handle in practical problems render the removal of noisy, redundant or useless instances a convenient first step for any real-world application. Many algorithms have been proposed for prototype selection. For difficult problems, however, the use of only a single method would unlikely achieve the desired performance. Similar to the problem of classification, ensembles of prototype selectors have been proposed to overcome the limitations of single algorithms. In ensembles of prototype selectors, the usual combination method is based on a voting scheme coupled with an acceptance threshold. However, this method is suboptimal, because the relationships among the prototypes are not taken into account. In this paper, we propose a different approach, in which we consider not only the number of times every prototype has been selected but also the subsets of prototypes that are selected. With this additional information we develop GEEBIES, which is a new way of combining the results of ensembles of prototype selectors. In a large set of problems, we show that our proposal outperforms the standard boosting approach. A way of scaling up our method to large datasets is also proposed and experimentally tested.