Emergency department free-text chief complaints (CCs) are a major data source for syndromic surveillance. CCs need to be classified into syndromic categories for subsequent automatic analysis. However, the lack of a standard vocabulary and high-quality encodings of CCs hinder effective classification. This paper presents a new ontology-enhanced automatic CC classification approach. Exploiting semantic relations in a medical ontology, this approach is motivated to address the CC vocabulary variation problem in general and to meet the specific need for a classification approach capable of handling multiple sets of syndromic categories. We report an experimental study comparing our approach with two popular CC classification methods using a real-world dataset. This study indicates that our ontology-enhanced approach performs significantly better than the benchmark methods in terms of sensitivity, F measure, and F2 measure.
Abstract:
A regulating agency in a government, i.e. regulator, must co-align its information systems (IS) planning strategy with executing agencies, i.e. executors, for better e-government performance. Using an established strategic co-alignment model, we analyze the mutual participating strategies between regulator and executors and examine the outcomes and performance associated with that coalignment choice. After conducting a large-scale survey study of government agencies in Taiwan, the co-alignment relationship between e-government IS policy regulator and executor is examined. Based on the findings, we discuss their implications for e-government research and practice.
Abstract:
The concept of corporate sustainability suggests that firms need to maintain sustainability principles and practices by addressing stakeholders' economic, ecological, and social concerns. Social media has become a knowledge depository where managers can evaluate stakeholders' concerns about the firm's sustainability-related issues. This study proposes a computational approach that utilizes natural language processing techniques to detect sustainability-related communities within online web forums. The validity of the detected communities was assessed based on their impacts on relevant firms' market performance when the firms' social responsibility was challenged. Experiments on three datasets showed that our system is effective in detecting sustainability-related communities. Also, a strong correlation was found between the activities of the identified sustainability-related communities and the firms' market performance during events that challenged the firms' social responsibilities. Our research contributes to the practice of managing corporate sustainability by facilitating managers in evaluating sustainability-related concerns of stakeholders and making effective managerial responses. © (2013) by the AIS/ICIS Administrative Office All rights reserved.
Abstract:
This paper reports our research in the Web page filtering process in specialized search engine development. We propose a machine-learning-based approach that combines Web content analysis and Web structure analysis. Instead of a bag of words, each Web page is represented by a set of content-based and link-based features, which can be used as the input for various machine learning algorithms. The proposed approach was implemented using both a feedforward/backpropagation neural network and a support vector machine. An evaluation study was conducted and showed that the proposed approaches performed better than the benchmark approaches. © Springer-Verlag Berlin Heidelberg 2004.
