Contextual bipolarity and its quality criteria in bipolar linguistic summaries

Bipolar linguistic summaries of data are assumed to be an extension of the ‘classical’ linguistic summarization, a data mining technique revealing complex patterns present in data in a human consistent form. The extension proposal is based on the possibilistic interpretation of the ‘and possibly’ operator and introduced notion of context, which results in the introduction of the new ‘contextual and possibly’ operator. As the end user is expecting the most relevant summaries, ways of determining the quality of summary propositions (quality measures) needs to be developed. Here we focus on specific insights into the quality measures of proposed bipolar linguistic summaries of data and present some basic examples of their correctness and necessity of introduction

Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project.

OBJECTIVES: We sought to use natural language processing to develop a suite of language models to capture key symptoms of severe mental illness (SMI) from clinical text, to facilitate the secondary use of mental healthcare data in research. DESIGN: Development and validation of information extraction applications for ascertaining symptoms of SMI in routine mental health records using the Clinical Record Interactive Search (CRIS) data resource; description of their distribution in a corpus of discharge summaries. SETTING: Electronic records from a large mental healthcare provider serving a geographic catchment of 1.2 million residents in four boroughs of south London, UK. PARTICIPANTS: The distribution of derived symptoms was described in 23 128 discharge summaries from 7962 patients who had received an SMI diagnosis, and 13 496 discharge summaries from 7575 patients who had received a non-SMI diagnosis. OUTCOME MEASURES: Fifty SMI symptoms were identified by a team of psychiatrists for extraction based on salience and linguistic consistency in records, broadly categorised under positive, negative, disorganisation, manic and catatonic subgroups. Text models for each symptom were generated using the TextHunter tool and the CRIS database. RESULTS: We extracted data for 46 symptoms with a median F1 score of 0.88. Four symptom models performed poorly and were excluded. From the corpus of discharge summaries, it was possible to extract symptomatology in 87% of patients with SMI and 60% of patients with non-SMI diagnosis. CONCLUSIONS: This work demonstrates the possibility of automatically extracting a broad range of SMI symptoms from English text discharge summaries for patients with an SMI diagnosis. Descriptive data also indicated that most symptoms cut across diagnoses, rather than being restricted to particular groups

An initial state of design and development of intelligent knowledge discovery system for stock exchange database

Data mining is a challenging matter in research field for the last few years.Researchers are using different techniques in data mining.This paper discussed the initial state of Design and Development Intelligent Knowledge Discovery System for Stock Exchange (SE) Databases. We divide our problem in two modules.In first module we define Fuzzy Rule Base System to determined vague information in stock exchange databases.After normalizing massive amount of data we will apply our proposed approach, Mining Frequent Patterns with Neural Networks.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely.In second module we will generate clustering algorithm. Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules

Validation of the "World Health Organization Disability Assessment Schedule, WHODAS-2" in patients with chronic diseases

Background: The WHODAS-2 is a disability assessment instrument based on the conceptual framework of the International Classification of Functioning, Disability, and Health (ICF). It provides a global measure of disability and 7 domain-specific scores. The aim of this study was to assess WHODAS-2 conceptual model and metric properties in a set of chronic and prevalent clinical conditions accounting for a wide scope of disability in Europe. Methods: 1,119 patients with one of 13 chronic conditions were recruited in 7 European centres. Participants were clinically evaluated and administered the WHODAS-2 and the SF-36 at baseline, 6 weeks and 3 months of follow-up. The latent structure was explored and confirmed by factor analysis (FA). Reliability was assessed in terms of internal consistency (Cronbach's alpha) and reproducibility (intra-class correlation coefficients, ICC). Construct validity was evaluated by correlating the WHODAS-2 and SF-36 domains, and comparing known groups based on the clinical-severity and work status. Effect size (ES) coefficient was used to assess responsiveness. To assess reproducibility and responsiveness, subsamples of stable (at 6 weeks) and improved (after 3 moths) patients were defined, respectively, according to changes in their clinical-severity. Results: The satisfactory FA goodness of fit indexes confirmed a second order factor structure with 7 dimensions, and a global score for the WHODAS-2. Cronbach's alpha ranged from 0.77 (self care) to 0.98 (life activities: work or school), and the ICC was lower, but achieved the recommended standard of 0.7 for four domains. Correlations between global WHODAS-2 score and the different domains of the SF-36 ranged from -0.29 to -0.65. Most of the WHODAS-2 scores showed statistically significant differences among clinical-severity groups for all pathologies, and between working patients and those not working due to ill health (p < 0.001). Among the subsample of patients who had improved, responsiveness coefficients were small to moderate (ES = 0.3-0.7), but higher than those of the SF-36. Conclusions: The latent structure originally designed by WHODAS-2 developers has been confirmed for the first time, and it has shown good metric properties in clinic and rehabilitation samples. Therefore, considerable support is provided to the WHODAS-2 utilization as an international instrument to measure disability based on the ICF model

Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

A study examining depression and bipolar support alliance online peer support groups

The current study examined the experiences of individuals who participated in one or more online peer support group (OPSG) sessions organized by Depression and Bipolar Support Alliance (DBSA) in order to elucidate whether participants experience higher levels of quality of life as associated with their participation in OPSG. Participants ranged in age from 18 to 79 and were primarily White/Caucasian and female. Participants completed a survey, which consisted of a demographics questionnaire and the Quality of Life Enjoyment and Satisfaction Questionnaire- Short Form. Data were collected using Qualtrics and analyzed using SPSS. No statistical significance was found regarding differences in quality of life according to whether participants attended a DBSA group in the past two months. However, results indicated that participants who were married or in a committed relationship indicated a significantly higher quality of life score (F(4, 91) = 3.89, p = .006) than other groups. The results of the present study were inconsistent with the current literature, which suggests a link between quality of life and participation in peer support. Additional unmeasured variables may have contributed to a null finding. In conclusion, results indicate that online peer support groups, specifically DBSA Online Peer Support Groups, may be unrelated quality of life

A corpus of science journalism for analyzing writing quality

We introduce a corpus of science journalism articles, categorized in three levels of writing quality. The corpus fulï¬lls a glaring need for realistic data on which applications concerned with predicting text quality can be developed and evaluated. In this article we describe how we identiï¬ed, guided by the judgements of renowned writers, samples of extraordinarily well-written pieces and how these were expanded to a larger set of typical journalistic writing. We provide details about the corpus and the text quality evaluations it can support. Our intention is to further extend the corpus with annotations of phenomena that reveal quantiï¬able differences between levels of writing quality. Here we introduce two of the many types of annotation on the sentence level that distinguish amazing from typical writing: text generality/speciï¬city and communicative goal. We explore the feasibility of acquiring annotations automatically, and verify that such features are indeed predictive of writing quality. We ï¬nd that the annotation of general/speciï¬c on sentence level can be performed reasonably accurately fully automatically, while automatic annotations of communicative goal reveals salient characteristics of journalistic writing but does not align with categories we wish to annotate in future work

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)