Models To Data Research Articles

Recommender System to Classify Antenatal Records of Pregnant Women

To develop a Recommender system for classifying antenatal records of pregnant women to extract patterns for the risk prediction for Gestational Diabetes Mellitus (GDM) using prediction models. To prevent complicated issues screening and diagnostic criteria must be adequate, timely, and efficient. The recommender system involves a novel approach that is practical, efficient, and patient and clinician-friendly in predicting adverse outcomes of GDM. Design three-tier architecture for pattern extraction using SPSS modeler for Bayesian classification and use TOAD Data Modeler tool allow users to visually create, maintain and document the data, prediction models and visualization tools for analyzing the patterns extracted from Bayesian prediction model. Process the system using Predictive analytic regression models. To implement alerts mechanism for clinical predictions, access antenatal outpatient records and the medication to Endocrinologists, Gynecologists and pregnant women.Moreover, our model aims to present a practical, inexpensive, efficient, reproducible, easy, and patient and clinician-friendly approach.

Factorization-based primary dimension modelling for multidimensional data in recommender systems

In recent years, multidimensional data is becoming increasingly popular in recommender systems. For example, social tagging systems encourage users to employ user-defined keywords to help manage content in a personalized way. Recommender systems built upon social tagging systems utilize social tagging data to improve recommendation accuracy. Context-aware recommender systems incorporate context information (e.g. time, location, weather, etc.) into recommendation models to make recommendations more personalized. User profiles play a crucial role in recommender systems because user profiles provide the information about users’ information needs based on which personalized recommendations can be generated. Existing user profiling techniques for collaborative filtering (CF) recommender systems based on multidimensional data mostly analyze data through splitting the multidimensional relations into lower-dimensional relations. However, this leads to the loss of multidimensionality in user-item interactions. A different type of methods for addressing multidimensional data is factorization techniques such as tensor factorization (TF), which discover holistic latent relations between all dimensions in multidimensional data. However, TF models are not constructed to analyze any particular dimension of multidimensional data. But for many application domains, certain dimensions are of particular interest such as the user dimension in recommender systems. However, user profiles are not explicitly reflected in TF models. In this paper, we propose a unified approach to profile any particular dimension in multidimensional data based on partially-decomposed Tucker models (TM). We prove that the proposed profiling approach is a compatible higher-order extension of the matrix factorization based approaches. Moreover, we prove that the proposed TM-based profiles can be updated incrementally with new data without reconstructing the entire profiles. We integrate the proposed TF-based user/item profiling approach into neighborhood-based collaborative filtering recommenders for making top-N item recommendations. Extensive experiments have been conducted using real-world social tagging datasets to demonstrate that the CF recommenders integrated with the proposed user/item profiles outperform state-of-the-art CF recommendation approaches in terms of recommendation accuracy.

Doc-Based Modelling for Medical Big Data

Doc-Based Modelling for Medical Big Data

Corporate environmental information system data storage development and management (Environmental Information System)

Abstract In this article a software implementation of the environmental monitoring is developed and presented, which is responsible for receive, store, process and analysis of data. For logical database design system Computer- Aided Software Engineering (CASE) technology, the AllFusion ERwin Data Modeler was selected. To develop corporate Oracle database management system used. The database contains a set of objects, which store all the primary and additional service information, as well as a set of software modules of business logic. The developed information system makes it possible to find optimal solutions for clean and disposal of the contaminated areas. There are advantages of created databases on the areas to be remediated, such as the analysis of remediation made by using plants.

Semiparametric statistical inferences for longitudinal data with nonparametric covariance modelling

ABSTRACTIn this paper, semiparametric modelling for longitudinal data with an unstructured error process is considered. We propose a partially linear additive regression model for longitudinal data in which within-subject variances and covariances of the error process are described by unknown univariate and bivariate functions, respectively. We provide an estimating approach in which polynomial splines are used to approximate the additive nonparametric components and the within-subject variance and covariance functions are estimated nonparametrically. Both the asymptotic normality of the resulting parametric component estimators and optimal convergence rate of the resulting nonparametric component estimators are established. In addition, we develop a variable selection procedure to identify significant parametric and nonparametric components simultaneously. We show that the proposed SCAD penalty-based estimators of non-zero components have an oracle property. Some simulation studies are conducted to examine the finite-sample performance of the proposed estimation and variable selection procedures. A real data set is also analysed to demonstrate the usefulness of the proposed method.

Visualising interactions in bi- and triadditive models for three-way tables

This paper concerns the visualisation of interaction in three-way arrays. It extends some standard ways of visualising biadditive modelling for two-way data to the case of three-way data. Three-way interaction is modelled by the Parafac method as applied to interaction arrays that have main effects and biadditive terms removed. These interactions are visualised in three and two dimensions. We introduce some ideas to reduce visual overload that can occur when the data array has many entries. Details are given on the interpretation of a novel way of representing rank-three interactions accurately in two dimensions. The discussion has implications regarding interpreting the concept of interaction in three-way arrays.

PAG2ADMG: A Novel Methodology to Enumerate Causal Graph Structures

Causal graphs, such as directed acyclic graphs (DAGs) and partial ancestral graphs (PAGs), represent causal relationships among variables in a model. Methods exist for learning DAGs and PAGs from data and for converting DAGs to PAGs. However, these methods only output a single causal graph consistent with the independencies/dependencies (the Markov equivalence class M) estimated from the data. However, many distinct graphs may be consistent with M, and a data modeler may wish to select among these using domain knowledge. In this paper, we present a method that makes this possible. We introduce PAG2ADMG, the first method for enumerating all causal graphs consistent with M, under certain assumptions. PAG2ADMG converts a given PAG into a set of acyclic directed mixed graphs (ADMGs). We prove the correctness of the approach and demonstrate its efficiency relative to brute-force enumeration.

Multilevel pharmacokinetics-driven modeling of metabolomics data

IntroductionMultilevel modeling is a quantitative statistical method to investigate variability and relationships between variables of interest, taking into account population structure and dependencies. It can be used for prediction, data reduction and causal inference from experiments and observational studies allowing for more efficient elucidation of knowledge.ObjectivesIn this study we introduced the concept of multilevel pharmacokinetics (PK)-driven modelling for large-sample, unbalanced and unadjusted metabolomics data comprising nucleoside and creatinine concentration measurements in urine of healthy and cancer patients.MethodsA Bayesian multilevel model was proposed to describe the nucleoside and creatinine concentration ratio considering age, sex and health status as covariates. The predictive performance of the proposed model was summarized via area under the ROC, sensitivity and specificity using external validation.ResultsCancer was associated with an increase in methylthioadenosine/creatinine excretion rate by a factor of 1.42 (1.09–2.03) which constituted the highest increase among all nucleosides. Age influenced nucleosides/creatinine excretion rates for all nucleosides in the same direction which was likely caused by a decrease in creatinine clearance with age. There was a small evidence of sex-related differences for methylthioadenosine. The individual a posteriori prediction of patient classification as area under the ROC with 5th and 95th percentile was 0.57(0.5–0.67) with sensitivity and specificity of 0.59(0.42–0.76) and 0.57(0.45–0.7), respectively suggesting limited usefulness of 13 nucleosides/creatinine urine concentration measurements in predicting disease in this population.ConclusionBayesian multilevel pharmacokinetics-driven modeling in metabolomics may be useful in understanding the data and may constitute a new tool for searching towards potential candidates of disease indicators.

Itemwise conditionally independent nonresponse modelling for incomplete multivariate data

We introduce a nonresponse mechanism for multivariate missing data in which each study variable and its nonresponse indicator are conditionally independent given the remaining variables and their nonresponse indicators. This is a nonignorable missingness mechanism, in that nonresponse for any item can depend on values of other items that are themselves missing. We show that under this itemwise conditionally independent nonresponse assumption, one can define and identify nonparametric saturated classes of joint multivariate models for the study variables and their missingness indicators. We also show how to perform sensitivity analysis with respect to violations of the conditional independence assumptions encoded by this missingness mechanism. We illustrate the proposed modelling approach with data analyses.

Robust estimators in semi-functional partial linear regression models

Partial linear models have been adapted to deal with functional covariates to capture both the advantages of a semi-linear modelling and those of nonparametric modelling for functional data. It is easy to see that the estimation procedures for these models are highly sensitive to the presence of even a small proportion of outliers in the data. To solve the problem of atypical observations when the covariates of the nonparametric component are functional, robust estimates for the regression parameter and regression operator are introduced. Consistency results of the robust estimators and the asymptotic distribution of the regression parameter estimator are studied. The reported numerical experiments show that the resulting estimators have good robustness properties. The benefits of considering robust estimators is also illustrated on a real data set where the robust fit reveals the presence of influential outliers.

Item response theory and structural equation modelling for ordinal data: Describing the relationship between KIDSCREEN and Life-H

Both item response theory and structural equation models are useful in the analysis of ordered categorical responses from health assessment questionnaires. We highlight the advantages and disadvantages of the item response theory and structural equation modelling approaches to modelling ordinal data, from within a community health setting. Using data from the SPARCLE project focussing on children with cerebral palsy, this paper investigates the relationship between two ordinal rating scales, the KIDSCREEN, which measures quality-of-life, and Life-H, which measures participation. Practical issues relating to fitting models, such as non-positive definite observed or fitted correlation matrices, and approaches to assessing model fit are discussed. item response theory models allow properties such as the conditional independence of particular domains of a measurement instrument to be assessed. When, as with the SPARCLE data, the latent traits are multidimensional, structural equation models generally provide a much more convenient modelling framework.

Logistic Regression Modelling for Complex Survey Data with an Application for Bed Net Use in Mozambique

Logistic Regression Models have been widely used in many areas of research, namely in health sciences, to study risk factors associated to diseases. Many population based surveys, such as Demographic and Health Survey (DHS), are constructed assuming complex sampling, i.e., probabilistic, stratified and multistage sampling, with unequal weights in the observations; this complex design must be taken into account in order to have reliable results. However, this very relevant issue usually is not well analyzed in the literature. The aim of the study is to specify the logistic regression model with complex sample design, and to demonstrate how to estimate it using the R software survey package. More specifically, we used Mozambique Demographic Health and Survey data 2011 (MDHS 2011) to illustrate how to correct for the effect of sample design in the particular case of estimating the risk factors associated to the probability of using mosquito bed nets. Our results show that in the presence of complex sampling, appropriate methods must be used both in descriptive and inferential statistics.

Bilgi İçeren Durdurma Varlığında Yinelemeli Olay Verisi İçin Yeni Bir Modelleme

Amaç: Bilgi içeren durdurma, yinelemeli olay verisinin modellemesinde dikkat edilmesi gereken önemli bir konudur. Bilgi içeren durdurma; ölüm gibi bir başarısızlık olayının gerçekleşmesi ya da bireylerin çalışmadan çekilmesi sonucu meydana gelir. Bu nedenle yinelemeli olay zamanları ile bilgi içeren durdurma zamanı ilişkilidir. Yinelemeli olay verisinde bu ilişkinin göz ardı edilmesi model parametrelerinin yanlı tahmin edilmesine yol açmaktadır. Bu çalışmanın amacı, bilgi içeren durdurma varlığında yinelemeli olay verisini modellemek ve parametre tahminlerini elde etmektir. Gereç ve Yöntemler: Bu çalışmada, bilgi içeren durdurmanın ölüm olayından kaynaklandığı durum incelenmiş ve ölüm olayının varlığında yinelemeli olay süreci homojen Poisson süreci ile modellenmiştir. Yinelemeli olay sürecine ilişkin yoğunluk fonksiyonu için yeni bir model önerilmiş ve yineleme zamanları ile ölüm zamanı arasındaki ilişki yapısı, paylaşılmış zayıflık modeli kullanılarak oluşturulmuştur. Önerilen model, farklı durdurma oranları ve örneklem büyüklüklerini içeren senaryolar altında bir benzetim çalışması ile incelenmiştir. Bilgi içeren durdurmanın dikkate alınmadığı indirgenmiş modelle karşılaştırmalar yapılmıştır. Bulgular: Benzetim çalışması sonucunda önerilen modelin Akaike Bilgi Kriteri (AIC) değerleri indirgenmiş modelden daha düşük bulunmuştur. Sonuç: Yapılan çalışmada ölüm gibi bir bilgi içeren durdurmanın söz konusu olduğu yinelemeli olay verisinin, ölüm olayı göz ardı edilerek modellenmesi parametre tahminlerini olumsuz etkilediği görülmüştür.

Parent–child inpatient treatment for children with behavioural and emotional disorders: a multilevel analysis of within-subjects effects

BackgroundThe importance of parental involvement in child treatment is well-established. Several child psychiatric clinics have, therefore, set up inpatient family units where children and parents are both actively involved in the treatment. Unfortunately, evidence supporting the benefits of these units is sparse.MethodsWe evaluated the effectiveness of inpatient treatment for families with severe parent–child interaction problems in a child psychiatric setting. Consecutive admissions to the parent–child ward (N = 66) were studied. A within-subjects design was used with four assessment points (baseline, admission, discharge, four-week follow-up). Outcome measures were 1) parent and teacher ratings of child behaviour, and 2) parent self-ratings of parenting practices, parental strains and parental mental health. Data were analyzed using multilevel modelling for longitudinal data (piecewise growth curve models).ResultsAll parent-rated measures improved significantly during the four-week treatment period (d = 0.4 – 1.3). These improvements were significantly greater than those observed during the four-week pre-admission period. In addition, benefits were maintained during the four-week follow-up period. Only parents’ self-efficacy in managing their child’s behaviour showed continued improvement during follow-up. Teacher ratings of children’s disruptive behaviour at school were stable during the pre-admission period and showed significant improvements at follow-up (d = 0.3 – 0.4).ConclusionsWe conclude that parent–child inpatient treatment has positive effects on child and parent behaviour and mental health, and can therefore be recommended for children with behavioural and emotional disorders and severe parent–child interaction problems.

Rederivation of the fuzzy–possibilistic clustering objective function through Bayesian inference

Unsupervised clustering of a set of datums into homogeneous groups is a primitive operation required in many signal and image processing applications. In fact, different incarnations and hybrids of Fuzzy C-Means (FCM) and Possibilistic C-means (PCM) have been suggested which address additional requirements such as accepting weighted sets and being robust to the presence of outliers. Nevertheless, arriving at a general framework, which is independent of the datum model and the notion of homogeneity of a particular problem class, is a challenge. However, this process has not been followed organically and clustering algorithms are generally based on exogenous objective functions which are heuristically engineered and are believed to lead to the satisfaction of a required behavior. These techniques also commonly depend on regularization coefficients which are to be set “prudently” by the user or through separate processes. In contrast, in this work, we utilize Bayesian inference and derive a robustified objective function for a fuzzy–possibilistic clustering algorithm by assuming a generic datum model and a generic notion of cluster homogeneity. We utilize this model for the purpose of cluster validity assessment as well. We emphasize the epistemological importance of the theoretical basis on which the developed methodology rests. At the end of this paper, we present experimental results to exhibit the utilization of the developed framework in the context of four different problem classes.

More precise methods for national research citation impact comparisons

Abstract Governments sometimes need to analyse sets of research papers within a field in order to monitor progress, assess the effect of recent policy changes, or identify areas of excellence. They may compare the average citation impacts of the papers by dividing them by the world average for the field and year. Since citation data is highly skewed, however, simple averages may be too imprecise to robustly identify differences within, rather than across, fields. In response, this article introduces two new methods to identify national differences in average citation impact, one based on linear modelling for normalised data and the other using the geometric mean. Results from a sample of 26 Scopus fields between 2009 and 2015 show that geometric means are the most precise and so are recommended for smaller sample sizes, such as for individual fields. The regression method has the advantage of distinguishing between national contributions to internationally collaborative articles, but has substantially wider confidence intervals than the geometric mean, undermining its value for any except the largest sample sizes.

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare.

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a “data modeler” tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets.

Interpreting physicochemical experimental data sets

With the wealth of experimental physicochemical data available to chemoinformaticians from the literature, commercial, and company databases an increasing challenge is the interpretation of such datasets. Subtle differences in experimental methodology used to generate these datasets can give rise to variations in physicochemical property values. Such methodology nuances will be apparent to an expert experimentalist but not necessarily to the data analyst and modeller. This paper describes the differences between common methodologies for measuring the four most important physicochemical properties namely aqueous solubility, octan-1-ol/water distribution coefficient, pK(a) and plasma protein binding highlighting key factors that can lead to systematic differences. Insight is given into how to identify datasets suitable for combining.

Analyzing bivariate ordinal data with CUB margins

Statistical modelling for ordinal data has received a considerable attention in the literature, and a consolidated theory relying on Generalized Linear Model approach has been developed. In this article, we present an innovative technique for modelling bivariate ordinal data. In particular, we consider the method introduced by Plackett for constructing a one-parameter bivariate distribution from given margins, and we apply it in order to represent correlated ordinal variables which individually follows a CUB model. This is a univariate mixture distribution defined by the convex Combination of a Uniform and a shifted Binomial distribution whose parameters may be related to rater's covariates. The article shows how the bivariate distribution can be defined and how its characterizing parameter, which describes the association between the component random variables, can be related to the subject's covariates. The proposed approach is applied to the study of two key drivers of extra virgin olive oil consumption in Italy. The technique allows a representation of the data whose meaning can be easily interpreted providing useful information for management support.

PRM237 - Bayesian Models for Cost-Effectiveness Analysis in the Presence of Structural Zero Costs

Bayesian modelling for cost-effectiveness data has received much attention in both the health economics and the statistical literature in recent years. Cost-effectiveness data are characterised by a relatively complex structure of relationships linking the suitable measure of clinical benefit (\eg QALYs) and the associated costs. Simplifying assumptions, such as (bivariate) normality of the underlying distributions are usually not granted, particularly for the cost variable, which is characterised by markedly skewed distributions. In addition, individual-level datasets are often characterised by the presence of structural zeros in the cost variable. Hurdle models can be used to account for the presence of excess zeros in a distribution and have been applied in the context of cost data. We extend their application to cost-effectiveness data, defining a full Bayesian model which consists of a selection model for the subjects with null costs, a marginal model for the costs and a conditional model for the measure of effectiveness (conditionally on the observed costs). The model is presented using a working example to describe its main features.