Doctor of Philosophy

dissertationMedical knowledge learned in medical school can become quickly outdated given the tremendous growth of the biomedical literature. It is the responsibility of medical practitioners to continuously update their knowledge with recent, best available clinical evidence to make informed decisions about patient care. However, clinicians often have little time to spend on reading the primary literature even within their narrow specialty. As a result, they often rely on systematic evidence reviews developed by medical experts to fulfill their information needs. At the present, systematic reviews of clinical research are manually created and updated, which is expensive, slow, and unable to keep up with the rapidly growing pace of medical literature. This dissertation research aims to enhance the traditional systematic review development process using computer-aided solutions. The first study investigates query expansion and scientific quality ranking approaches to enhance literature search on clinical guideline topics. The study showed that unsupervised methods can improve retrieval performance of a popular biomedical search engine (PubMed). The proposed methods improve the comprehensiveness of literature search and increase the ratio of finding relevant studies with reduced screening effort. The second and third studies aim to enhance the traditional manual data extraction process. The second study developed a framework to extract and classify texts from PDF reports. This study demonstrated that a rule-based multipass sieve approach is more effective than a machine-learning approach in categorizing document-level structures and iv that classifying and filtering publication metadata and semistructured texts enhances the performance of an information extraction system. The proposed method could serve as a document processing step in any text mining research on PDF documents. The third study proposed a solution for the computer-aided data extraction by recommending relevant sentences and key phrases extracted from publication reports. This study demonstrated that using a machine-learning classifier to prioritize sentences for specific data elements performs equally or better than an abstract screening approach, and might save time and reduce errors in the full-text screening process. In summary, this dissertation showed that there are promising opportunities for technology enhancement to assist in the development of systematic reviews. In this modern age when computing resources are getting cheaper and more powerful, the failure to apply computer technologies to assist and optimize the manual processes is a lost opportunity to improve the timeliness of systematic reviews. This research provides methodologies and tests hypotheses, which can serve as the basis for further large-scale software engineering projects aimed at fully realizing the prospect of computer-aided systematic reviews

A Survey on Region Extractors from Web Documents

Extracting information from web documents has become a research area in which new proposals sprout out year after year. This has motivated several researchers to work on surveys that attempt to provide an overall picture of the many existing proposals. Unfortunately, none of these surveys provide a complete picture, because they do not take region extractors into account. These tools are kind of preprocessors, because they help information extractors focus on the regions of a web document that contain relevant information. With the increasing complexity of web documents, region extractors are becoming a must to extract information from many websites. Beyond information extraction, region extractors have also found their way into information retrieval, focused web crawling, topic distillation, adaptive content delivery, mashups, and metasearch engines. In this paper, we survey the existing proposals regarding region extractors and compare them side by side.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08- TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Economía, Industria y Competitividad TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-

Baby Boomers Retiring: Strategies for Small Businesses Retaining Explicit and Tacit Knowledge

More than 35% of the U.S. workforce is composed of Baby Boomers who are eligible to retire within the next 5 years. Despite the potential loss of critical expertise, a gap in knowledge retention exists in small consulting businesses. The purpose of this case study was to explore effective strategies for retaining the tacit and explicit knowledge of retiring employees, to avoid operational knowledge drain. Exploration ensued through semistructured interviews at 2 small consulting businesses in the Washington, DC metropolitan area that are adept at innovatively retaining requisite knowledge. The conceptual frameworks of Bass\u27 transformational leadership and Nonaka\u27s knowledge creation led to the identification of strategies to retain tacit and explicit knowledge of retiring Baby Boomers. Seven small business leaders addressed questions on knowledge types, knowledge stimulation and sharing methods, and retention strategies to provide meaningful responses to the knowledge retention phenomenon. Data analysis included the Colaizzi and modified van Kaam methods of mining, categorizing, organizing, and describing participants\u27 statements. Subsequently, the themes that emerged during the analysis identified reward, communication, and motivation as strategies for knowledge-share and transfer. Succession planning, mentoring, documentation, training, and knowledge sharing also emerged as effective methods for knowledge retention. The findings will contribute to social change by illuminating the roles effective leaders practice to influence and foster knowledge management, offering insight to other small businesses having difficulties remaining sustainable as the operational knowledge of Baby Boomers becomes unavailable as they retire

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Pick-n-mix approaches to technology supply : XML as a standard “glue” linking universalised locals

We report on our experiences in a participatory design project to develop ICTs in a hospital ward working with deliberate self-harm patients. This project involves the creation and constant re-creation of sociotechnical ensembles in which XML-related technologies may come to play vital roles. The importance of these technologies arises from the aim underlying the project of creating systems that are shaped in locally meaningful ways but reach beyond their immediate context to gain wider importance. We argue that XML is well placed to play the role of "glue" that binds multiple such systems together. We analyse the implications of localised systems development for technology supply and argue that inscriptions that are evident in XML-related standards are and will be very important for the uptake of XML technologies

An Information Extraction Approach to Reorganizing and Summarizing Specifications

Materials and Process Specifications are complex semi-structured documents containing numeric data, text, and images. This article describes a coarse-grain extraction technique to automatically reorganize and summarize spec content. Specifically, a strategy for semantic-markup, to capture content within a semantic ontology, relevant to semi-automatic extraction, has been developed and experimented with. The working prototypes were built in the context of Cohesia\u27s existing software infrastructure, and use techniques from Information Extraction, XML technology, etc