A unification of models for meta-analysis of diagnostic accuracy studies

Studies of diagnostic accuracy require more sophisticated methods for their meta-analysis than studies of therapeutic interventions. A number of different, and apparently divergent, methods for meta-analysis of diagnostic studies have been proposed, including two alternative approaches that are statistically rigorous and allow for between-study variability: the hierarchical summary receiver operating characteristic (ROC) model (Rutter and Gatsonis, 2001) and bivariate random-effects meta-analysis (van Houwelingen and others, 1993), (van Houwelingen and others, 2002), (Reitsma and others, 2005). We show that these two models are very closely related, and define the circumstances in which they are identical. We discuss the different forms of summary model output suggested by the two approaches, including summary ROC curves, summary points, confidence regions, and prediction regions.

Age and gender differences in health-related quality of life of children and adolescents in Europe: a multilevel analysis

OBJECTIVES: To determine age and gender differences in health-related quality of life (HRQOL) in children and adolescents across 12 European countries using a newly developed HRQOL measure (KIDSCREEN). METHODS: The KIDSCREEN-52 questionnaire was filled in by 21,590 children and adolescents aged 8-18 from 12 countries. We used multilevel regression analyses to model the hierarchical structure of the data. In addition, effect sizes were computed to test for gender differences within each age group. RESULTS: Children generally showed better HRQOL than adolescents (P < 0.001). While boys and girls had similar HRQOL at young age, girls' HRQOL declined more than boys' (P < 0.001) with increasing age, depending on the HRQOL scale. There was significant variation between countries both at the youngest age and for age trajectories. CONCLUSIONS: For the first time, gender and age differences in children's and adolescents' HRQOL across Europe were assessed using a comprehensive and standardised instrument. Gender and age differences exist for most HRQOL scales. Differences in HRQOL across Europe point to the importance of national contexts for youth's well-being.

Incorporating Latent Variables into Discrete Choice Models - A Simultaneous Estimation Approach Using SEM Software

Integrated choice and latent variable (ICLV) models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM) for latent variables. Despite their conceptual appeal, applications of ICLV models in marketing remain rare. We extend previous ICLV applications by first estimating a multinomial choice model and, second, by estimating hierarchical relations between latent variables. An empirical study on travel mode choice clearly demonstrates the value of ICLV models to enhance the understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonism as well as attitudes such as a desire for flexibility impact on travel mode choice. Furthermore, we show that it is possible to estimate such a complex ICLV model with the widely available structural equation modeling package Mplus. This finding is likely to encourage more widespread application of this appealing model class in the marketing field.

Effect of nonnormal random components on level and power in group-randomized trials

The use of group-randomized trials is particularly widespread in the evaluation of health care, educational, and screening strategies. Group-randomized trials represent a subset of a larger class of designs often labeled nested, hierarchical, or multilevel and are characterized by the randomization of intact social units or groups, rather than individuals. The application of random effects models to group-randomized trials requires the specification of fixed and random components of the model. The underlying assumption is usually that these random components are normally distributed. This research is intended to determine if the Type I error rate and power are affected when the assumption of normality for the random component representing the group effect is violated. ^ In this study, simulated data are used to examine the Type I error rate, power, bias and mean squared error of the estimates of the fixed effect and the observed intraclass correlation coefficient (ICC) when the random component representing the group effect possess distributions with non-normal characteristics, such as heavy tails or severe skewness. The simulated data are generated with various characteristics (e.g. number of schools per condition, number of students per school, and several within school ICCs) observed in most small, school-based, group-randomized trials. The analysis is carried out using SAS PROC MIXED, Version 6.12, with random effects specified in a random statement and restricted maximum likelihood (REML) estimation specified. The results from the non-normally distributed data are compared to the results obtained from the analysis of data with similar design characteristics but normally distributed random effects. ^ The results suggest that the violation of the normality assumption for the group component by a skewed or heavy-tailed distribution does not appear to influence the estimation of the fixed effect, Type I error, and power. Negative biases were detected when estimating the sample ICC and dramatically increased in magnitude as the true ICC increased. These biases were not as pronounced when the true ICC was within the range observed in most group-randomized trials (i.e. 0.00 to 0.05). The normally distributed group effect also resulted in bias ICC estimates when the true ICC was greater than 0.05. However, this may be a result of higher correlation within the data. ^

The roles of religiosity and affluence for adolescents' family orientation: Multilevel analyses of 18 cultures

Recent sociological and psychological debates concern the nature of the relation between changing religious beliefs and changing significance of the family. The current study analyzes multilevel relations between religiosity (personal and culture-level) and several aspects of family orientation for n = 4902 adolescents from 18 nations/areas from diverse cultural contexts covering a number of religious denominations with data from the Value-of-Children-Study (Trommsdorff & Nauck, 2005). In addition, cultural values from the World Values Survey representing religious versus secular values as well as survival versus self- expression values are examined at the cultural level of analysis as a joint effect with nation-level economic development. Results showed that religiosity/religious values were positively related to all aspects of adolescents’ family orientation at the individual as well as the cultural level, while societal affluence was only related to a loss of importance of the traditional and hierarchical aspects of family orientation. Postmaterialist self-expression values were unrelated to adolescents’ family orientation.

The enduring effects of psychodynamic treatments vis-à-vis alternative treatments: A multilevel longitudinal meta-analysis

Although evidence suggests that the benefits of psychodynamic treatments are sustained over time, presently it is unclear whether these sustained benefits are superior to non-psychodynamic treatments. Additionally, the extant literature comparing the sustained benefits of psychodynamic treatments compared to alternative treatments is limited with methodological shortcomings. The purpose of the current study was to conduct a rigorous test of the growth of the benefits of psychodynamic treatments relative to alternative treatments across distinct domains of change (i.e., all outcome measures, targeted outcome measures, non-targeted outcome measures, and personality outcome measures). To do so, the study employed strict inclusion criteria to identify randomized clinical trials that directly compared at least one bona fide psychodynamic treatment and one bona fide non-psychodynamic treatment. Hierarchical linear modeling (Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2011) was used to longitudinally model the impact of psychodynamic treatments compared to non-psychodynamic treatments at post-treatment and to compare the growth (i.e., slope) of effects beyond treatment completion. Findings from the present meta-analysis indicated that psychodynamic treatments and non-psychodynamic treatments were equally efficacious at post-treatment and at follow-up for combined outcomes (k=20), targeted outcomes (k=19), non-targeted outcomes (k=17), and personality outcomes (k=6). Clinical implications, directions for future research, and limitations are discussed.

The association of area socioeconomic status and cancer screening: A systematic review and multilevel study

Background. The gap between actual and ideal rates of routine cancer screening in the U.S., particularly for colorectal cancer screening (CRCS) (1;2), is responsible for an unnecessary burden of morbidity and mortality, particularly for disadvantaged groups. Knowledge about the effects of individual and area influences is being advanced by a growing body of research that has examined the association of area socioeconomic status (SES) and cancer screening after controlling for individual SES. The findings from this emerging and heterogeneous research in the cancer screening literature have been mixed. Moreover, multilevel studies in this area have not yet adequately explored the possibility of differential associations by population subgroup, despite some evidence suggesting gender-specific effects. ^ Objectives and methods. This dissertation reports on a systematic review of studies on the association of area SES and cancer screening and a multilevel study of the association between area SES and CRCS. The specific aims of the systematic review are to: (1) describe the study designs, constructs, methods, and measures; (2) describe the association of area SES and cancer screening; and (3) identify neglected areas of research. ^ The empiric study linked a pooled sample of respondents aged ≥50 years without a personal history of colorectal cancer from the 2003 and 2005 California Health Interview Surveys with a comprehensive set of census-tract level area SES measures from the 2000 U.S. Census. Two-level random intercept models were used to test 2 hypotheses: (1) area SES will be associated with adherence to two modalities of CRCS after controlling for individual SES; and (2) gender will moderate the relationship between area socioeconomic status and adherence to both modalities of CRCS. ^ Results. The systematic review identified 19 eligible studies that demonstrated variability in study designs, methods, constructs, and measures. The majority of tested associations were either not statistically significant or significant and in the positive direction, indicating that as area SES increased, the odds of CRCS increased. The multilevel study demonstrated that while multiple aspects of area SES were associated with CRCS after controlling for individual SES, associations differed by screening modality and in the case of endoscopy, they also differed by gender. ^ Conclusions. Conceptual and methodologic heterogeneity and weaknesses in the literature to date limit definitive conclusions about the underlying relationships between area SES and cancer screening. The multilevel study provided partial support for both hypotheses. Future research should continue to explore the role of gender as a moderating influence with the aim of identifying the mechanisms linking area SES and cancer prevention behaviors. ^

HPV vaccine coverage in Texas counties: An application of multilevel, small area estimation

Health departments, research institutions, policy-makers, and healthcare providers are often interested in knowing the health status of their clients/constituents. Without the resources, financially or administratively, to go out into the community and conduct health assessments directly, these entities frequently rely on data from population-based surveys to supply the information they need. Unfortunately, these surveys are ill-equipped for the job due to sample size and privacy concerns. Small area estimation (SAE) techniques have excellent potential in such circumstances, but have been underutilized in public health due to lack of awareness and confidence in applying its methods. The goal of this research is to make model-based SAE accessible to a broad readership using clear, example-based learning. Specifically, we applied the principles of multilevel, unit-level SAE to describe the geographic distribution of HPV vaccine coverage among females aged 11-26 in Texas.^ Multilevel (3 level: individual, county, public health region) random-intercept logit models of HPV vaccination (receipt of ≥ 1 dose Gardasil® ) were fit to data from the 2008 Behavioral Risk Factor Surveillance System (outcome and level 1 covariates) and a number of secondary sources (group-level covariates). Sampling weights were scaled (level 1) or constructed (levels 2 & 3), and incorporated at every level. Using the regression coefficients (and standard errors) from the final models, I simulated 10,000 datasets for each regression coefficient from the normal distribution and applied them to the logit model to estimate HPV vaccine coverage in each county and respective demographic subgroup. For simplicity, I only provide coverage estimates (and 95% confidence intervals) for counties.^ County-level coverage among females aged 11-17 varied from 6.8-29.0%. For females aged 18-26, coverage varied from 1.9%-23.8%. Aggregated to the state level, these values translate to indirect state estimates of 15.5% and 11.4%, respectively; both of which fall within the confidence intervals for the direct estimates of HPV vaccine coverage in Texas (Females 11-17: 17.7%, 95% CI: 13.6, 21.9; Females 18-26: 12.0%, 95% CI: 6.2, 17.7).^ Small area estimation has great potential for informing policy, program development and evaluation, and the provision of health services. Harnessing the flexibility of multilevel, unit-level SAE to estimate HPV vaccine coverage among females aged 11-26 in Texas counties, I have provided (1) practical guidance on how to conceptualize and conduct modelbased SAE, (2) a robust framework that can be applied to other health outcomes or geographic levels of aggregation, and (3) HPV vaccine coverage data that may inform the development of health education programs, the provision of health services, the planning of additional research studies, and the creation of local health policies.^

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

A multidisciplinary reference framework to support innovative design, implementation and assessment of chronic care models

Hoy en día, por primera vez en la historia, la mayor parte de la población podrá vivir hasta los sesenta años y más (United Nations, 2015). Sin embargo, todavía existe poca evidencia que demuestre que las personas mayores, estén viviendo con mejor salud que sus padres, a la misma edad, ya que la mayoría de los problemas de salud en edades avanzadas están asociados a las enfermedades crónicas (WHO, 2015). Los sistemas sanitarios de los países desarrollados funcionan adecuadamente cuando se trata del cuidado de enfermedades agudas, pero no son lo suficientemente eficaces en la gestión de las enfermedades crónicas. Durante la última década, se han realizado esfuerzos para mejorar esta gestión, por medio de la utilización de estrategias de prevención y de reenfoque de la provisión de los servicios de atención para la salud (Kane et al. 2005). Según una revisión sistemática de modelos de cuidado de salud, comisionada por el sistema nacional de salud Británico, pocos modelos han conceptualizado cuáles son los componentes que hay que utilizar para proporcionar un cuidado crónico efectivo, y estos componentes no han sido suficientemente estructurados y articulados. Por lo tanto, no hay suficiente evidencia sobre el impacto real de cualquier modelo existente en la actualidad (Ham, 2006). Las innovaciones podrían ayudar a conseguir mejores diagnósticos, tratamientos y gestión de pacientes crónicos, así como a dar soporte a los profesionales y a los pacientes en el cuidado. Sin embargo, la forma en las que estas innovaciones se proporcionan no es lo suficientemente eficiente, efectiva y amigable para el usuario. Para mejorar esto, hace falta crear equipos de trabajo y estrategias multidisciplinares. En conclusión, hacen falta actividades que permitan conseguir que las innovaciones sean utilizadas en los sistemas de salud que quieren mejorar la gestión del cuidado crónico, para que sea posible: 1) traducir la “atención sanitaria basada en la evidencia” en “conocimiento factible”; 2) hacer frente a la complejidad de la atención sanitaria a través de una investigación multidisciplinaria; 3) identificar una aproximación sistemática para que se establezcan intervenciones innovadoras en el cuidado de salud. El marco de referencia desarrollado en este trabajo de investigación es un intento de aportar estas mejoras. Las siguientes hipótesis han sido propuestas: Hipótesis 1: es posible definir un proceso de traducción que convierta un modelo de cuidado crónico en una descripción estructurada de objetivos, requisitos e indicadores clave de rendimiento. Hipótesis 2: el proceso de traducción, si se ejecuta a través de elementos basados en la evidencia, multidisciplinares y de orientación económica, puede convertir un modelo de cuidado crónico en un marco descriptivo, que define el ciclo de vida de soluciones innovadoras para el cuidado de enfermedades crónicas. Hipótesis 3: es posible definir un método para evaluar procesos, resultados y capacidad de desarrollar habilidades, y asistir equipos multidisciplinares en la creación de soluciones innovadoras para el cuidado crónico. Hipótesis 4: es posible dar soporte al desarrollo de soluciones innovadoras para el cuidado crónico a través de un marco de referencia y conseguir efectos positivos, medidos en indicadores clave de rendimiento. Para verificar las hipótesis, se ha definido una aproximación metodológica compuesta de cuatro Fases, cada una asociada a una hipótesis. Antes de esto, se ha llevado a cabo una “Fase 0”, donde se han analizado los antecedentes sobre el problema (i.e. adopción sistemática de la innovación en el cuidado crónico) desde una perspectiva multi-dominio y multi-disciplinar. Durante la fase 1, se ha desarrollado un Proceso de Traducción del Conocimiento, elaborado a partir del JBI Joanna Briggs Institute (JBI) model of evidence-based healthcare (Pearson, 2005), y sobre el cual se han definido cuatro Bloques de Innovación. Estos bloques consisten en una descripción de elementos innovadores, definidos en la fase 0, que han sido añadidos a los cuatros elementos que componen el modelo JBI. El trabajo llevado a cabo en esta fase ha servido también para definir los materiales que el proceso de traducción tiene que ejecutar. La traducción que se ha llevado a cabo en la fase 2, y que traduce la mejor evidencia disponible de cuidado crónico en acción: resultado de este proceso de traducción es la parte descriptiva del marco de referencia, que consiste en una descripción de un modelo de cuidado crónico (se ha elegido el Chronic Care Model, Wagner, 1996) en términos de objetivos, especificaciones e indicadores clave de rendimiento y organizada en tres ciclos de innovación (diseño, implementación y evaluación). Este resultado ha permitido verificar la segunda hipótesis. Durante la fase 3, para demostrar la tercera hipótesis, se ha desarrollado un método-mixto de evaluación de equipos multidisciplinares que trabajan en innovaciones para el cuidado crónico. Este método se ha creado a partir del método mixto usado para la evaluación de equipo multidisciplinares translacionales (Wooden, 2013). El método creado añade una dimensión procedural al marco. El resultado de esta fase consiste, por lo tanto, en una primera versión del marco de referencia, lista para ser experimentada. En la fase 4, se ha validado el marco a través de un caso de estudio multinivel y con técnicas de observación-participante como método de recolección de datos. Como caso de estudio se han elegido las actividades de investigación que el grupo de investigación LifeStech ha desarrollado desde el 2008 para mejorar la gestión de la diabetes, actividades realizadas en un contexto internacional. Los resultados demuestran que el marco ha permitido mejorar las actividades de trabajo en distintos niveles: 1) la calidad y cantidad de las publicaciones; 2) se han conseguido dos contratos de investigación sobre diabetes: el primero es un proyecto de investigación aplicada, el segundo es un proyecto financiado para acelerar las innovaciones en el mercado; 3) a través de los indicadores claves de rendimiento propuestos en el marco, una prueba de concepto de un prototipo desarrollado en un proyecto de investigación ha sido transformada en una evaluación temprana de una intervención eHealth para el manejo de la diabetes, que ha sido recientemente incluida en Repositorio de prácticas innovadoras del Partenariado de Innovación Europeo en Envejecimiento saludable y activo. La verificación de las 4 hipótesis ha permitido demonstrar la hipótesis principal de este trabajo de investigación: es posible contribuir a crear un puente entre la atención sanitaria y la innovación y, por lo tanto, mejorar la manera en que el cuidado crónico sea procurado en los sistemas sanitarios. ABSTRACT Nowadays, for the first time in history, most people can expect to live into their sixties and beyond (United Nations, 2015). However, little evidence suggests that older people are experiencing better health than their parents, and most of the health problems of older age are linked to Chronic Diseases (WHO, 2015). The established health care systems in developed countries are well suited to the treatment of acute diseases but are mostly inadequate for dealing with CDs. Healthcare systems are challenging the burden of chronic diseases by putting more emphasis on the prevention of disease and by looking for new ways to reorient the provision of care (Kane et al., 2005). According to an evidence-based review commissioned by the British NHS Institute, few models have conceptualized effective components of care for CDs and these components have been not structured and articulated. “Consequently, there is limited evidence about the real impact of any of the existing models” (Ham, 2006). Innovations could support to achieve better diagnosis, treatment and management for patients across the continuum of care, by supporting health professionals and empowering patients to take responsibility. However, the way they are delivered is not sufficiently efficient, effective and consumer friendly. The improvement of innovation delivery, involves the creation of multidisciplinary research teams and taskforces, rather than just working teams. There are several actions to improve the adoption of innovations from healthcare systems that are tackling the epidemics of CDs: 1) Translate Evidence-Based Healthcare (EBH) into actionable knowledge; 2) Face the complexity of healthcare through multidisciplinary research; 3) Identify a systematic approach to support effective implementation of healthcare interventions through innovation. The framework proposed in this research work is an attempt to provide these improvements. The following hypotheses have been drafted: Hypothesis 1: it is possible to define a translation process to convert a model of chronic care into a structured description of goals, requirements and key performance indicators. Hypothesis 2: a translation process, if executed through evidence-based, multidisciplinary, holistic and business-oriented elements, can convert a model of chronic care in a descriptive framework, which defines the whole development cycle of innovative solutions for chronic disease management. Hypothesis 3: it is possible to design a method to evaluate processes, outcomes and skill acquisition capacities, and assist multidisciplinary research teams in the creation of innovative solutions for chronic disease management. Hypothesis 4: it is possible to assist the development of innovative solutions for chronic disease management through a reference framework and produce positive effects, measured through key performance indicators. In order to verify the hypotheses, a methodological approach, composed of four Phases that correspond to each one of the stated hypothesis, was defined. Prior to this, a “Phase 0”, consisting in a multi-domain and multi-disciplinary background analysis of the problem (i.e.: systematic adoption of innovation to chronic care), was carried out. During phase 1, in order to verify the first hypothesis, a Knowledge Translation Process (KTP) was developed, starting from the JBI Joanna Briggs Institute (JBI) model of evidence-based healthcare was used (Pearson, 2005) and adding Four Innovation Blocks. These blocks represent an enriched description, added to the JBI model, to accelerate the transformation of evidence-healthcare through innovation; the innovation blocks are built on top of the conclusions drawn after Phase 0. The background analysis gave also indication on the materials and methods to be used for the execution of the KTP, carried out during phase 2, that translates the actual best available evidence for chronic care into action: this resulted in a descriptive Framework, which is a description of a model of chronic care (the Chronic Care Model was chosen, Wagner, 1996) in terms of goals, specified requirements and Key Performance Indicators, and articulated in the three development cycles of innovation (i.e. design, implementation and evaluation). Thanks to this result the second hypothesis was verified. During phase 3, in order to verify the third hypothesis, a mixed-method to evaluate multidisciplinary teams working on innovations for chronic care, was created, based on a mixed-method used for the evaluation of Multidisciplinary Translational Teams (Wooden, 2013). This method adds a procedural dimension to the descriptive component of the Framework, The result of this phase consisted in a draft version of the framework, ready to be tested in a real scenario. During phase 4, a single and multilevel case study, with participant-observation data collection, was carried out, in order to have a complete but at the same time multi-sectorial evaluation of the framework. The activities that the LifeStech research group carried out since 2008 to improve the management of diabetes have been selected as case study. The results achieved showed that the framework allowed to improve the research activities in different directions: the quality and quantity of the research publications that LifeStech has issued, have increased substantially; 2 project grants to improve the management of diabetes, have been assigned: the first is a grant funding applied research while the second is about accelerating innovations into the market; by using the assessment KPIs of the framework, the proof of concept validation of a prototype developed in a research project was transformed into an early stage assessment of innovative eHealth intervention for Diabetes Management, which has been recently included in the repository of innovative practice of the European Innovation Partnership on Active and Health Ageing initiative. The verification of the 4 hypotheses lead to verify the main hypothesis of this research work: it is possible to contribute to bridge the gap between healthcare and innovation and, in turn, improve the way chronic care is delivered by healthcare systems.

Assessing advertising in a hierarchical decision model

Many destination marketing organizations in the United States and elsewhere are facing budget retrenchment for tourism marketing, especially for advertising. This study evaluates a three-stage model using Random Coefficient Logit (RCL) approach which controls for correlations between different non-independent alternatives and considers heterogeneity within individual’s responses to advertising. The results of this study indicate that the proposed RCL model results in a significantly better fit as compared to traditional logit models, and indicates that tourism advertising significantly influences tourist decisions with several variables (age, income, distance and Internet access) moderating these decisions differently depending on decision stage and product type. These findings suggest that this approach provides a better foundation for assessing, and in turn, designing more effective advertising campaigns.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

Methods to Control for Bias in Observational Studies

Thesis (Master's)--University of Washington, 2016-06

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.

A hierarchical latent variable model for data visualization

Visualization has proven to be a powerful and widely-applicable tool the analysis and interpretation of data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and sub-clusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach first on a toy data set, and then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multi-phase flows in oil pipelines and to data in 36 dimensions derived from satellite images.