Using Neural Networks for Relation Extraction from Biomedical Literature

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1

What can management theories offer evidence-based practice? A comparative analysis of measurement tools for organisational context

Background: Given the current emphasis on networks as vehicles for innovation and change in health service delivery, the ability to conceptualise and measure organisational enablers for the social construction of knowledge merits attention. This study aimed to develop a composite tool to measure the organisational context for evidence-based practice (EBP) in healthcare. Methods: A structured search of the major healthcare and management databases for measurement tools from four domains: research utilisation (RU), research activity (RA), knowledge management (KM), and organisational learning (OL). Included studies were reports of the development or use of measurement tools that included organisational factors. Tools were appraised for face and content validity, plus development and testing methods. Measurement tool items were extracted, merged across the four domains, and categorised within a constructed framework describing the absorptive and receptive capacities of organisations. Results: Thirty measurement tools were identified and appraised. Eighteen tools from the four domains were selected for item extraction and analysis. The constructed framework consists of seven categories relating to three core organisational attributes of vision, leadership, and a learning culture, and four stages of knowledge need, acquisition of new knowledge, knowledge sharing, and knowledge use. Measurement tools from RA or RU domains had more items relating to the categories of leadership, and acquisition of new knowledge; while tools from KM or learning organisation domains had more items relating to vision, learning culture, knowledge need, and knowledge sharing. There was equal emphasis on knowledge use in the different domains. Conclusion: If the translation of evidence into knowledge is viewed as socially mediated, tools to measure the organisational context of EBP in healthcare could be enhanced by consideration of related concepts from the organisational and management sciences. Comparison of measurement tools across domains suggests that there is scope within EBP for supplementing the current emphasis on human and technical resources to support information uptake and use by individuals. Consideration of measurement tools from the fields of KM and OL shows more content related to social mechanisms to facilitate knowledge recognition, translation, and transfer between individuals and groups

Robust input representations for low-resource information extraction

Recent advances in the field of natural language processing were achieved with deep learning models. This led to a wide range of new research questions concerning the stability of such large-scale systems and their applicability beyond well-studied tasks and datasets, such as information extraction in non-standard domains and languages, in particular, in low-resource environments. In this work, we address these challenges and make important contributions across fields such as representation learning and transfer learning by proposing novel model architectures and training strategies to overcome existing limitations, including a lack of training resources, domain mismatches and language barriers. In particular, we propose solutions to close the domain gap between representation models by, e.g., domain-adaptive pre-training or our novel meta-embedding architecture for creating a joint representations of multiple embedding methods. Our broad set of experiments demonstrates state-of-the-art performance of our methods for various sequence tagging and classification tasks and highlight their robustness in challenging low-resource settings across languages and domains.Die jüngsten Fortschritte auf dem Gebiet der Verarbeitung natürlicher Sprache wurden mit Deep-Learning-Modellen erzielt. Dies führte zu einer Vielzahl neuer Forschungsfragen bezüglich der Stabilität solcher großen Systeme und ihrer Anwendbarkeit über gut untersuchte Aufgaben und Datensätze hinaus, wie z. B. die Informationsextraktion für Nicht-Standardsprachen, aber auch Textdomänen und Aufgaben, für die selbst im Englischen nur wenige Trainingsdaten zur Verfügung stehen. In dieser Arbeit gehen wir auf diese Herausforderungen ein und leisten wichtige Beiträge in Bereichen wie Repräsentationslernen und Transferlernen, indem wir neuartige Modellarchitekturen und Trainingsstrategien vorschlagen, um bestehende Beschränkungen zu überwinden, darunter fehlende Trainingsressourcen, ungesehene Domänen und Sprachbarrieren. Insbesondere schlagen wir Lösungen vor, um die Domänenlücke zwischen Repräsentationsmodellen zu schließen, z.B. durch domänenadaptives Vortrainieren oder unsere neuartige Meta-Embedding-Architektur zur Erstellung einer gemeinsamen Repräsentation mehrerer Embeddingmethoden. Unsere umfassende Evaluierung demonstriert die Leistungsfähigkeit unserer Methoden für verschiedene Klassifizierungsaufgaben auf Word und Satzebene und unterstreicht ihre Robustheit in anspruchsvollen, ressourcenarmen Umgebungen in verschiedenen Sprachen und Domänen

Prospect patents, data markets, and the commons in data-driven medicine : openness and the political economy of intellectual property rights

Scholars who point to political influences and the regulatory function of patent courts in the USA have long questioned the courts’ subjective interpretation of what ‘things’ can be claimed as inventions. The present article sheds light on a different but related facet: the role of the courts in regulating knowledge production. I argue that the recent cases decided by the US Supreme Court and the Federal Circuit, which made diagnostics and software very difficult to patent and which attracted criticism for a wealth of different reasons, are fine case studies of the current debate over the proper role of the state in regulating the marketplace and knowledge production in the emerging information economy. The article explains that these patents are prospect patents that may be used by a monopolist to collect data that everybody else needs in order to compete effectively. As such, they raise familiar concerns about failure of coordination emerging as a result of a monopolist controlling a resource such as datasets that others need and cannot replicate. In effect, the courts regulated the market, primarily focusing on ensuring the free flow of data in the emerging marketplace very much in the spirit of the ‘free the data’ language in various policy initiatives, yet at the same time with an eye to boost downstream innovation. In doing so, these decisions essentially endorse practices of personal information processing which constitute a new type of public domain: a source of raw materials which are there for the taking and which have become most important inputs to commercial activity. From this vantage point of view, the legal interpretation of the private and the shared legitimizes a model of data extraction from individuals, the raw material of information capitalism, that will fuel the next generation of data-intensive therapeutics in the field of data-driven medicine