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Depression is a global health concern. Social networks allow the affected population to share their experiences. These experiences, when mined, extracted, and analyzed, can be converted into either warnings to recall drugs (dangerous side effects), or service improvement (interventions, treatment options) based on observations derived from user behavior in depression-related social networks. Our aim...
In this study, we address two major problems of spontaneous reporting systems for adverse drug events (ADEs): underreporting and low report content quality. In the scope of WEB-RADR project, we make use of relevant patient information available in electronic health record (EHR) systems to facilitate ADE reporting process and promote spontaneous reporting on mobile devices. By semi-automatically extracting...
Online health forums provide a large repository for patients, caregivers, and researchers to seek valuable information. The extraction of patient-reported personal health experience from the forums has many important applications. For example, medical researchers can discover trustable knowledge from the extracted experience. Patients can search for peers with similar experience and connect with them...
The enormous amounts of data that are continuously recorded in electronic health record systems offer ample opportunities for data science applications to improve healthcare. There are, however, challenges involved in using such data for machine learning, such as high dimensionality and sparsity, as well as an inherent heterogeneity that does not allow the distinct types of clinical data to be treated...
Named Entity Recognition (NER) is a crucial step in text mining. This paper proposes a new graph-based technique for representing unstructured medical text. The new representation is used to extract discriminative features that are able to enhance the NER performance. To evaluate the usefulness of the proposed graph-based technique, the i2b2 medication challenge data set is used. Specifically, the...
As a new tool of data analysis and knowledge processing, formal concept analysis has drawn more and more attention in various fields. This research focuses on prescriptions of GuiZhi Decoction, which is from Zhongjing Zhang's Treatise on Cold Pathogenic Diseases. First, we constructed the database for prescriptions of GuiZhi Decoction based on the prescriptions' names, the concept extraction and formal...
In order to solve the problems that how to mine and express classification knowledge and rules in compatibility of prescription, this paper introduces a new theory of formal concept analysis (FCA), and realizes the compatibility of prescription knowledge mining. Meanwhile, the prescription drugs of reverting yin disease treatment in Treatise on febrile diseases are selected to apply the FCA theory...
Nowadays, the classification of graph data has become an important and active research topic in the last decade, which has a wide variety of real world applications, e.g. drug activity predictions and kinase inhibitor discovery. Current research on graph classification focuses on single-label settings. However, in many applications, each graph data can be assigned with a set of multiple labels simultaneously...
In this paper, we present xDUCON a framework for coordinating and enforcing usage control policies across different collaborating organisations. xDUCON allows the specification of usage control policies that concisely capture conditions, authorisations, and obligations on both providers and consumers of resources. The xDUCON framework is based on the Shared Data Space (SDS) abstraction, where collaborating...
Comparative and evaluative question answering (QA) systems provide objective answers to questions that involve comparisons and evaluations based on a quantifiable set of criteria. As evaluations involve inferences and computations, answers are not lifted from source text. This entails the need for correct semantic interpretation of comparative expressions, converting them to quantifiable criteria...
The approach taken with OGEP is to parse relevant domain data in the form of unstructured content (or corpus) and use that knowledge to generate and/or evolve an existing ontology. OGEP creates a constant conversation between the corpus parser and a reasoning mechanism (corpus reasoner) that continually formulates potential ontology modifications in the form of hypotheses. These hypotheses are weighted...
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