Joint Entity Extraction and Assertion Detection for Clinical Text

Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for information extraction. Most of the existing systems treat this task as a pipeline of two separate tasks, i.e., named entity recognition (NER) and rule-based negation detection. We consider this as a multi-task problem and present a novel end-to-end neural model to jointly extract entities and negations. We extend a standard hierarchical encoder-decoder NER model and first adopt a shared encoder followed by separate decoders for the two tasks. This architecture performs considerably better than the previous rule-based and machine learning-based systems. To overcome the problem of increased parameter size especially for low-resource settings, we propose the Conditional Softmax Shared Decoder architecture which achieves state-of-art results for NER and negation detection on the 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.Comment: Accepted at the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019

Detecting false-positive disease references in veterinary clinical notes without manual annotations

Clinicians often include references to diseases in clinical notes, which have not been diagnosed in their patients. For some diseases terms, the majority of disease references written in the patient notes may not refer to true disease diagnosis. These references occur because clinicians often use their clinical notes to speculate about disease existence (differential diagnosis) or to state that the disease has been ruled out. To train classifiers for disambiguating disease references, previous researchers built training sets by manually annotating sentences. We show how to create very large training sets without the need for manual annotation. We obtain state-of- the-art classification performance with a bidirectional long short-term memory model trained to distinguish disease references between patients with or without the disease diagnosis in veterinary clinical notes

Real-time classifiers from free-text for continuous surveillance of small animal disease

A wealth of information of epidemiological importance is held within unstructured narrative clinical records. Text mining provides computational techniques for extracting usable information from the language used to communicate between humans, including the spoken and written word. The aim of this work was to develop text-mining methodologies capable of rendering the large volume of information within veterinary clinical narratives accessible for research and surveillance purposes. The free-text records collated within the dataset of the Small Animal Veterinary Surveillance Network formed the development material and target of this work. The efficacy of pre-existent clinician-assigned coding applied to the dataset was evaluated and the nature of notation and vocabulary used in documenting consultations was explored and described. Consultation records were pre-processed to improve human and software readability, and software was developed to redact incidental identifiers present within the free-text. An automated system able to classify for the presence of clinical signs, utilising only information present within the free-text record, was developed with the aim that it would facilitate timely detection of spatio-temporal trends in clinical signs. Clinician-assigned main reason for visit coding provided a poor summary of the large quantity of information exchanged during a veterinary consultation and the nature of the coding and questionnaire triggering further obfuscated information. Delineation of the previously undocumented veterinary clinical sublanguage identified common themes and their manner of documentation, this was key to the development of programmatic methods. A rule-based classifier using logically-chosen dictionaries, sequential processing and data-masking redacted identifiers while maintaining research usability of records. Highly sensitive and specific free-text classification was achieved by applying classifiers for individual clinical signs within a context-sensitive scaffold, this permitted or prohibited matching dependent on the clinical context in which a clinical sign was documented. The mean sensitivity achieved within an unseen test dataset was 98.17 (74.47, 99.9)% and mean specificity 99.94 (77.1, 100.0)%. When used in combination to identify animals with any of a combination of gastrointestinal clinical signs, the sensitivity achieved was 99.44% (95% CI: 98.57, 99.78)% and specificity 99.74 (95% CI: 99.62, 99.83). This work illustrates the importance, utility and promise of free-text classification of clinical records and provides a framework within which this is possible whilst respecting the confidentiality of client and clinician

Picturing the Invisible

Picturing the Invisible presents different disciplinary approaches to articulating the invisible, that which is not known or that which is not provable. The challenge that we have seen is how to articulate these concepts, not only to those within a particular academic field but beyond, to other disciplines and society at large. As our understanding of the complexity of the world grows incrementally, so does our realisation that issues and problems can rarely be resolved within neat demarcations. Therefore, the importance of finding means of communicating across disciplines and fields becomes a priority. Whilst acknowledging the essential importance of the specialist academic, the capacity to understand other disciplines, their priorities, methodologies and even the language used can become crucial in being an effective instrument for change. This book brings together insights from leading academics from a wide range of disciplines including Art and Design, Curatorial Practice, Literature, Forensic Science, Fashion, Medical Science, Psychoanalysis and Psychotherapy, Philosophy, Astrophysics and Architecture with a shared interest in exploring how, in each discipline, we strive to find expression for the invisible or unknown, and to draw out and articulate some of the explicit and tacit ways of communicating those concepts that transcends traditional disciplinary boundaries

Picturing the Invisible: Exploring interdisciplinary synergies from the arts and the sciences

Picturing the Invisible presents different disciplinary approaches to articulating the invisible, that which is not known or that which is not provable. The challenge that we have seen is how to articulate these concepts, not only to those within a particular academic field but beyond, to other disciplines and society at large. As our understanding of the complexity of the world grows incrementally, so does our realisation that issues and problems can rarely be resolved within neat demarcations. Therefore, the importance of finding means of communicating across disciplines and fields becomes a priority. Whilst acknowledging the essential importance of the specialist academic, the capacity to understand other disciplines, their priorities, methodologies and even the language used can become crucial in being an effective instrument for change. This book brings together insights from leading academics from a wide range of disciplines including Art and Design, Curatorial Practice, Literature, Forensic Science, Medical Science, Psychoanalysis and Psychotherapy, Philosophy, Astrophysics and Architecture with a shared interest in exploring how, in each discipline, we strive to find expression for the invisible or unknown, and to draw out and articulate some of the explicit and tacit ways of communicating those concepts that transcend traditional disciplinary boundaries

Picturing the Invisible

Picturing the Invisible presents different disciplinary approaches to articulating the invisible, that which is not known or that which is not provable. The challenge that we have seen is how to articulate these concepts, not only to those within a particular academic field but beyond, to other disciplines and society at large. As our understanding of the complexity of the world grows incrementally, so does our realisation that issues and problems can rarely be resolved within neat demarcations. Therefore, the importance of finding means of communicating across disciplines and fields becomes a priority. Whilst acknowledging the essential importance of the specialist academic, the capacity to understand other disciplines, their priorities, methodologies and even the language used can become crucial in being an effective instrument for change. This book brings together insights from leading academics from a wide range of disciplines including Art and Design, Curatorial Practice, Literature, Forensic Science, Fashion, Medical Science, Psychoanalysis and Psychotherapy, Philosophy, Astrophysics and Architecture with a shared interest in exploring how, in each discipline, we strive to find expression for the invisible or unknown, and to draw out and articulate some of the explicit and tacit ways of communicating those concepts that transcends traditional disciplinary boundaries

Beyond Quantity: Research with Subsymbolic AI

How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately

The Gene Ontology Handbook

bioinformatics; biotechnolog