Improving average ranking precision in user searches for biomedical research datasets

Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorisation method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries. Our system provides competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP among the participants, being +22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. Our similarity measure algorithm seems to be robust, in particular compared to Divergence From Randomness framework, having smaller performance variations under different training conditions. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. In particular, the use of data driven query expansion methods could be an alternative to the complexity of biomedical terminologies

MEDLINE Search Retrieval Issues: A Longitudinal Query Analysis of Five Vendor Platforms

This study compared the results of data collected from a longitudinal query analysis of the MEDLINE database hosted on multiple platforms that include PubMed, EBSCOHost, Ovid, ProQuest, and Web of Science. The goal was to identify variations among the search results on the platforms after controlling for search query syntax. We devised twenty-nine cases of search queries comprised of five semantically equivalent queries per case to search against the five MEDLINE database platforms. We ran our queries monthly for a year and collected search result count data to observe changes. We found that search results varied considerably depending on MEDLINE platform. Reasons for variations were due to trends in scholarly publication such as publishing individual papers online first versus complete issues. Some other reasons were metadata differences in bibliographic records; differences in the levels of specificity of search fields provided by the platforms and large fluctuations in monthly search results based on the same query. Database integrity and currency issues were observed as each platform updated its MEDLINE data throughout the year. Specific biomedical bibliographic databases are used to inform clinical decision-making, create systematic reviews, and construct knowledge bases for clinical decision support systems. They serve as essential information retrieval and discovery tools to help identify and collect research data and are used in a broad range of fields and as the basis of multiple research designs. This study should help clinicians, researchers, librarians, informationists, and others understand how these platforms differ and inform future work in their standardization

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

PubFocus: semantic MEDLINE/PubMed citations analytics through integration of controlled biomedical dictionaries and ranking algorithm

BACKGROUND: Understanding research activity within any given biomedical field is important. Search outputs generated by MEDLINE/PubMed are not well classified and require lengthy manual citation analysis. Automation of citation analytics can be very useful and timesaving for both novices and experts. RESULTS: PubFocus web server automates analysis of MEDLINE/PubMed search queries by enriching them with two widely used human factor-based bibliometric indicators of publication quality: journal impact factor and volume of forward references. In addition to providing basic volumetric statistics, PubFocus also prioritizes citations and evaluates authors' impact on the field of search. PubFocus also analyses presence and occurrence of biomedical key terms within citations by utilizing controlled vocabularies. CONCLUSION: We have developed citations' prioritisation algorithm based on journal impact factor, forward referencing volume, referencing dynamics, and author's contribution level. It can be applied either to the primary set of PubMed search results or to the subsets of these results identified through key terms from controlled biomedical vocabularies and ontologies. NCI (National Cancer Institute) thesaurus and MGD (Mouse Genome Database) mammalian gene orthology have been implemented for key terms analytics. PubFocus provides a scalable platform for the integration of multiple available ontology databases. PubFocus analytics can be adapted for input sources of biomedical citations other than PubMed

A Study to Understand and Compare Evidence Based Practice Among Health Professionals Involved in Pain Management

Pain management is a common concern of multiple health professionals. Evidence-based practice (EBP) in pain management is a recognized approach used to improve health outcomes. EBP tools can facilitate its implementation. PAIN+ is a tool that provides access to pre-appraised current best research evidence on pain to support clinical decisions. It is important to understand the knowledge, attitudes and behavior of professionals towards EBP and more specifically how they access research about pain management. The overarching purpose of this thesis is to better understand how clinicians from different professions involved in pain management view EBP and implement specific strategies to find pain related research evidence. We conducted a series of studies incorporating various methods to address these questions. Data was collected supplementary to a large randomized control trial to compare “Push” vs. “Pull” strategies for uptake of pain research. In the first study, we compared the knowledge, attitudes, outcomes expectations and behaviors of physicians, nurses, physiotherapists, occupational therapists and psychologists towards EBP in pain management using a validated knowledge attitude and behavior (KABQ) questionnaire. In the second study, we used a mixed methods approach to understand the competencies of clinicians accessing electronic databases to search for evidence on pain management. In the third study, we performed a structured classification of the abstracts that were viewed by clinicians to understand their access behaviors. In the last part of the thesis, we compared the usefulness of PAIN+ with PubMed using a randomized crossover trial approach. The results of this thesis indicate that the professionals involved in pain management have good knowledge of and attitudes towards EBP, but behavior i.e. implementation of EBP in practice and perception of outcomes of implementing EBP were low. In the second study, we found that professionals had acceptable levels of basic literature searching skills but had low levels of use of more advanced skills, and were not aware of using clinical queries in their search. In the third study, we found that all professionals accessed research evidence when provided alerts about pain research and some variations in the types of studies accessed were observed. Differences in access behaviors might reflect differences in professional approach to pain management. In our fourth study the crossover randomized controlled trial; we found PAIN+ and PubMed were both rated useful in retrieving pain evidence for clinicians. Professionals showed an interest in evidence-based pain management, but their skills for finding evidence were limited, they appeared to need training in locating and appraising pain related research evidence, and may benefit from tools that reduce this burden

Creating the essential links for educating the evidence-based medical practitioner of the 21st century

A new postgraduate Medical (MBBS) program at the University of Queensland has been the catalyst for the development of a range of University of Queensland (UQ) Cybrary initiatives, in areas of information resources, services and support. Over the past seven years the UQ Cybrary has successfully integrated library services into the problem-based learning and the e-learning environment of the MBBS Program. Information and communications technology developments have been harnessed by the Cybrary to support the needs of the Program which are dispersed throughout the vast state of Queensland. In particular, there has been a focus on using information and communications technology (ICT) to provide efficient and equitable access for all those involved with UQ city and rural health education. The Cybrary has risen to the challenge of providing information services and resources to support evidence-based practice (EBP) and lifelong learning, ultimately contributing towards achieving an outstanding medical workforce for the 21st Century. This paper will outline how the UQ Cybrary has addressed these issues, particularly in the areas of integration of online materials in the e-learning environment, the development and delivery of tailored information literacy programs and the use of ICT to support access to information and services