Building a semantically annotated corpus of clinical texts

In this paper, we describe the construction of a semantically annotated corpus of clinical texts for use in the development and evaluation of systems for automatically extracting clinically significant information from the textual component of patient records. The paper details the sampling of textual material from a collection of 20,000 cancer patient records, the development of a semantic annotation scheme, the annotation methodology, the distribution of annotations in the final corpus, and the use of the corpus for development of an adaptive information extraction system. The resulting corpus is the most richly semantically annotated resource for clinical text processing built to date, whose value has been demonstrated through its use in developing an effective information extraction system. The detailed presentation of our corpus construction and annotation methodology will be of value to others seeking to build high-quality semantically annotated corpora in biomedical domains

Spectators’ aesthetic experiences of sound and movement in dance performance

In this paper we present a study of spectators’ aesthetic experiences of sound and movement in live dance performance. A multidisciplinary team comprising a choreographer, neuroscientists and qualitative researchers investigated the effects of different sound scores on dance spectators. What would be the impact of auditory stimulation on kinesthetic experience and/or aesthetic appreciation of the dance? What would be the effect of removing music altogether, so that spectators watched dance while hearing only the performers’ breathing and footfalls? We investigated audience experience through qualitative research, using post-performance focus groups, while a separately conducted functional brain imaging (fMRI) study measured the synchrony in brain activity across spectators when they watched dance with sound or breathing only. When audiences watched dance accompanied by music the fMRI data revealed evidence of greater intersubject synchronisation in a brain region consistent with complex auditory processing. The audience research found that some spectators derived pleasure from finding convergences between two complex stimuli (dance and music). The removal of music and the resulting audibility of the performers’ breathing had a significant impact on spectators’ aesthetic experience. The fMRI analysis showed increased synchronisation among observers, suggesting greater influence of the body when interpreting the dance stimuli. The audience research found evidence of similar corporeally focused experience. The paper discusses possible connections between the findings of our different approaches, and considers the implications of this study for interdisciplinary research collaborations between arts and sciences

A BRIEF BUT INTENSIVE LANGUAGE-LITERACY INTERVENTION FOR AN ADOLESCENT

The current service delivery model most frequently used in a school setting involves short, infrequent sessions over a 180-day school year. To date, there is no research that supports the current service delivery model as being the most effective and efficient model of intervention. As students transition from elementary to middle school, this model is particularly problematic for the adolescent student because of a rotating school schedule, increasing language demands of the academic curriculum, and development of self-perception and academic self-concept. A brief but intensive language-literacy intervention that takes place outside of the school year may be an effective and efficient alternative to adolescents who struggle with written language. The purpose of this study was to determine whether an adolescent who participates in a two-week intensive language-literacy intervention program would make significant gains in written narrative composition, complexity and accuracy of sentence composition, and encoding/decoding skills. Additionally, the investigator wished to determine whether or not an adolescent would demonstrate an increase in self-perception of literacy skills following participation in the aforementioned program. A multiple-baseline design across behaviors was used to examine written narratives collected from the adolescent during each session. There were four phases in this experiment: Baseline Phase - baseline data were collected; Phase A- intervention focused on discourse level literacy skills; Phase B- intervention focused on sentence level and discourse level skills; and Phase C- intervention focused on word/morpheme level, sentence level, and discourse level skills. In addition, pre and post test data were collected to examine word, sentence, and discourse level writing skills as well as self-perception of literacy skills. Preliminary results suggest a brief but intensive intervention did result in significant gains in language-literacy skills and self-perception of literacy skills. Further investigation is needed to determine if a gains can be generalized into the academic setting. Future studies in which the intensity of the intervention is manipulated (e.g. three weeks instead of two, a cycles approach addressing various aspects of language, etc.) could provide even stronger evidence for intervention programs of varied intensity

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.Siirretty Doriast

Supporting early oral language skills for English language learners in inner city preschool provision

BACKGROUND: A significant number of children now enter formal education in England with reduced levels of proficiency in oral language. Children who come from disadvantaged backgrounds and who are English language learners (ELL) are at risk of limited oral language skills in English which impacts on later educational achievement. AIMS: This paper reports the development of a theoretically motivated oral language intervention, Talking Time, designed to meet the needs of preschool children with poor language skills in typical preschool provision. SAMPLE: One hundred and forty-two 4-year-old children attending three inner city preschools in a disadvantaged area of London, England. METHOD: This is a quasi-experimental intervention study comparing children exposed to Talking Time with children exposed to a contrast intervention and children receiving the statutory early years curriculum. Measures were taken of both targeted and non-targeted language and cognitive skills. RESULTS: Data were analysed for the ELL. The intervention had a significant effect on vocabulary, oral comprehension, and sentence repetition but not narrative skills. As predicted, there were no effects on the skills which were not targeted. CONCLUSIONS: Regular evidence-based oral language interactions can make significant improvements in children's oral language. There is a need to examine the efficacy of more intensive interventions to raise language skills to allow learners to access the curriculum

Narrative Language as an Expression of Individual and Group Identity

Scientific Narrative Psychology integrates quantitative methodologies into the study of identity. Its methodology, Narrative Categorical Analysis, and its toolkit, NarrCat, were both originally developed by the Hungarian Narrative Psychology Group. NarrCat is for machine-made transformation of sentences in self-narratives into psychologically relevant, statistically processable narrative categories. The main body of this flexible and comprehensive system is formed by Psycho-Thematic modules, such as Agency, Evaluation, Emotion, Cognition, Spatiality, and Temporality. The Relational Modules include Social References, Semantic Role Labeling (SRL), and Negation. Certain elements can be combined into Hypermodules, such as Psychological Perspective and Spatio-Temporal Perspective, which allow for even more complex, higher level exploration of composite psychological processes. Using up-to-date developments of corpus linguistics and Natural Language Processing (NLP), a unique feature of NarrCat is its capacity of SRL. The structure of NarrCat, as well as the empirical results in group identity research, is discussed

Ethical issues of electronic patient data and informatics in clinical trial settings

The field of cancer bio-informatics unites the disciplines of scientific and clinical research withclinical practice and the treatment of individual patients. There is a need to study patients andsometimes their families, over many decades, to follow disease progress and long-term outcomes.This may require research teams to access the routinely-collected health data from generalpractice and hospital health records, prior to and after the cancer diagnosis is made. This clinicalinformation will increasingly include data provided by patients or acquired from them throughwearable devices that can monitor or deliver treatment, and data acquired from genetic relativesof the patient.All of these data, whether explicitly collected for the purpose of a clinical study, or routinelycollected as part of a patient?s life-time healthcare journey, are personal health data. There areethical and legal requirements to manage these data with care. This chapter explores the ethicalrequirements for collecting, holding, analysing and sharing personal health data, and thelegislation covering such activities