5,576 research outputs found

    Improving diagnostic procedures for epilepsy through automated recording and analysis of patients’ history

    Get PDF
    Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach

    Complexity Science in Human Change

    Get PDF
    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Ratio und similitudo: die vernunftkonforme Argumentation im Dialogus des Petrus Alfonsi

    Full text link
    Anders als in religionspolemischen Werken früherer Autoren, die auf die exegetische Diskussion zentriert sind, argumentiert Petrus Alfonsi in seinem Dialogus (um 1110 verfasst) gleichermaßen auf der Grundlage von auctoritas (der Bibel) und von ratio. In diesem Beitrag wird diskutiert, wie Petrus Alfonsi die vernunftbasierte Argumentation begrifflich fasst und umsetzt. An einer Stelle präzisiert Petrus Alfonsi drei Quellen der rationalen Erkenntnis. Die Aussage wird anhand einer genauen Lektüre und durch die Heranziehung der Quelle, das Werk des jüdischen Philosophen Saadia Gaon, Emunoth we-Deoth, interpretiert. Petrus Alfonsi unterscheidet darin die spontane Erkenntnis durch die Sinne, die deduktive Argumentation auf der Grundlage von allgemein anerkannten Prämissen (necessariae rationes) und die similitudo, die sich als die evidenzbasierte Argumentation verstehen lässt. Im Dialogus argumentiert Petrus Alfonsi nur selten auf der Grundlage von Prämissen, immer wieder findet sich eine Argumentation, die auf beobachtbaren Phänomenen basiert. Häufig legt Petrus Erkenntnisse der Naturphilosophie dar, die er durch Naturbespiele erläutert. Für dieses Verfahren setzt er auch den Begriff similitudo ein

    Constitutions of Value

    Get PDF
    Gathering an interdisciplinary range of cutting-edge scholars, this book addresses legal constitutions of value. Global value production and transnational value practices that rely on exploitation and extraction have left us with toxic commons and a damaged planet. Against this situation, the book examines law’s fundamental role in institutions of value production and valuation. Utilising pathbreaking theoretical approaches, it problematizes mainstream efforts to redeem institutions of value production by recoupling them with progressive values. Aiming beyond radical critique, the book opens up the possibility of imagining and enacting new and different value practices. This wide-ranging and accessible book will appeal to international lawyers, socio-legal scholars, those working at the intersections of law and economy and others, in politics, economics, environmental studies and elsewhere, who are concerned with rethinking our current ideas of what has value, what does not, and whether and how value may be revalued

    Measuring the Severity of Depression from Text using Graph Representation Learning

    Get PDF
    The common practice of psychology in measuring the severity of a patient's depressive symptoms is based on an interactive conversation between a clinician and the patient. In this dissertation, we focus on predicting a score representing the severity of depression from such a text. We first present a generic graph neural network (GNN) to automatically rate severity using patient transcripts. We also test a few sequence-based deep models in the same task. We then propose a novel form for node attributes within a GNN-based model that captures node-specific embedding for every word in the vocabulary. This provides a global representation of each node, coupled with node-level updates according to associations between words in a transcript. Furthermore, we evaluate the performance of our GNN-based model on a Twitter sentiment dataset to classify three different sentiments and on Alzheimer's data to differentiate Alzheimer’s disease from healthy individuals respectively. In addition to applying the GNN model to learn a prediction model from the text, we provide post-hoc explanations of the model's decisions for all three tasks using the model's gradients

    Authoritarianism and Subject Formation in Post-Independence Egypt: Egyptian Literature and Western Social Theory in Dialogue

    Get PDF
    The study grew out of a desire to examine how it feels to be denied what Hannah Arendt famously referred to as the ‘right to have rights,’ including the right to disobey. More specifically, this study seeks to understand how people living under particular regimes of power—characterised by distinct politics of fear, uncertainty, and silence—feel, define, and express themselves in relation to power, whether in the form of submission or resistance. In other words: How do authoritarian power dynamics affect individuals’ perception of self and how does it play into and shape the everyday life of the individual? At the heart of this inquiry is the notion of the subject, which forms both the conceptual foundation and the central focus of this study. The study draws primarily on the theoretical contributions of Michel Foucault, Giorgio Agamben, and Hannah Arendt on the interplay of power, resistance, and subjectivity. To frame the discussion, a socio-historical examination of post-independence power practices in Egypt and their impact on the constitution of the political subject is conducted. Research data is generated through an art-inspired qualitative research approach, primarily using Egyptian novels as a source of data to uncover the nuances and interiorities of the process of subject formation. Through a dialogue between Western social theory and Egyptian literature, the study provides an understanding of power practice in Egypt from 1952 to the present, particularly at the level of the inner panorama of the self in society and expands it into a reading of social and political theories on the question of power, subjectivity, resistance, and agency. The study is divided into six main chapters, including an introduction and a conclusion. Each empirical chapter of this study tells the story of a particular episode in time and is somewhat self-contained, yet all chapters are connected into a large coherent reading of modern Egyptian power practices. Just as the novels examined in this study tell a story with their words, so does my research. The study concludes that the process of subject formation in Egypt should be understood as an artefact of historical continuity that connects the past to the present, not necessarily in a linear fashion, but in a way that gives it a genealogical context, and as a dynamic process of shifting subject positions. The study further argues for the limitations of the status conception of citizenship as a defining framework for the state—society relationship in the context under study and proposes instead the use of the power—subject framework as a substitute. Last but not least, the study suggests that the connection between theory and method, expressed in the very structure of the research, reveals the epistemic relevance of literature to the conceptual imagination, contributing in a sense, to the discussion of the decolonisation of knowledge production. In some ways, this interdisciplinarity underscores the sheer breadth and hybridity of the concept of subject formation that has become apparent throughout this analysis. Keywords— Power, Subject Formation, Subjectivity, Egyptian Literature, Resistance, Agenc

    Sarcasm Detection in a Disaster Context

    Full text link
    During natural disasters, people often use social media platforms such as Twitter to ask for help, to provide information about the disaster situation, or to express contempt about the unfolding event or public policies and guidelines. This contempt is in some cases expressed as sarcasm or irony. Understanding this form of speech in a disaster-centric context is essential to improving natural language understanding of disaster-related tweets. In this paper, we introduce HurricaneSARC, a dataset of 15,000 tweets annotated for intended sarcasm, and provide a comprehensive investigation of sarcasm detection using pre-trained language models. Our best model is able to obtain as much as 0.70 F1 on our dataset. We also demonstrate that the performance on HurricaneSARC can be improved by leveraging intermediate task transfer learning. We release our data and code at https://github.com/tsosea2/HurricaneSarc

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

    Full text link
    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
    • …
    corecore