ORCID Profile
0000-0003-0271-5563
Current Organisation
University of Queensland
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Library and Information Studies | Information Retrieval and Web Search |
Electronic Information Storage and Retrieval Services | Health not elsewhere classified
Publisher: ACM
Date: 27-06-2018
Publisher: ACM
Date: 12-09-2016
Publisher: ACM
Date: 09-08-2023
Publisher: ACM
Date: 24-07-2011
Publisher: Springer International Publishing
Date: 2020
Publisher: Wiley
Date: 26-02-2014
DOI: 10.1002/ASI.23065
Publisher: ACM
Date: 27-02-2023
Publisher: Springer Science and Business Media LLC
Date: 20-11-2016
Publisher: ACM Press
Date: 2013
Publisher: ACM
Date: 15-12-2022
Publisher: ACM
Date: 15-12-2022
Publisher: ACM
Date: 17-10-2015
Publisher: ACM
Date: 15-12-2022
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer International Publishing
Date: 2021
Publisher: ACM
Date: 18-07-2023
Publisher: ACM
Date: 09-08-2015
Publisher: ACM
Date: 09-08-2023
Publisher: ACM
Date: 07-07-2016
Publisher: Oxford University Press (OUP)
Date: 07-08-2016
DOI: 10.1093/JAMIA/OCV069
Abstract: Objective This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined. Materials and methods The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional random fields as the supervised method, and least confidence and information density as 2 selection criteria for active learning framework were used. The effect of incremental learning vs standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. The following 2 clinical data sets were used for evaluation: the Informatics for Integrating Biology and the Bedside/Veteran Affairs (i2b2/VA) 2010 natural language processing challenge and the Shared Annotated Resources/Conference and Labs of the Evaluation Forum (ShARe/CLEF) 2013 eHealth Evaluation Lab. Results The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared with the random s ling baseline, the saving is at least doubled. Conclusion Incremental active learning is a promising approach for building effective and robust medical concept extraction models while significantly reducing the burden of manual annotation.
Publisher: Association for Computing Machinery (ACM)
Date: 07-06-2012
Abstract: In this thesis we investigate the use of quantum probability theory for ranking documents. Quantum probability theory is used to estimate the probability of relevance of a document given a user's query. We posit that quantum probability theory can lead to a better estimation of the probability of a document being relevant to a user's query than the common IR approach, i. e. the Probability Ranking Principle (PRP), which is based upon Kolmogorovian probability theory. Following our hypothesis, we formulate an analogy between the document retrieval scenario and a physical scenario, that of the double slit experiment. Through the analogy, we propose a novel ranking approach, the quantum probability ranking principle (qPRP). Key to our proposal is the presence of quantum interference. Mathematically, this is the statistical deviation between empirical observations and expected values predicted by the Kolmogorovian rule of additivity of probabilities of disjoint events in configurations such that of the double slit experiment. While PRP explicitly assumes that the relevancy of a document is independent of that of other documents, we suggest that qPRP implicitly models interdependent document relevance through quantum interference and thus is suited to those document ranking tasks where the independence assumption fails. Throughout the thesis, we also suggest how quantum interference can be estimated for effective document ranking. To validate our proposal and to gain more insights about approaches for document ranking, we (1) analyse PRP, qPRP and other ranking approaches, exposing the assumptions underlying their ranking criteria and formulating the conditions for the optimality of the two ranking principles, (2) empirically compare three ranking principles (i. e. PRP, interactive PRP, and qPRP) and two state-of-the-art ranking strategies in two retrieval scenarios, those of ad-hoc retrieval and ersity retrieval, (3) analytically contrast the ranking criteria of the examined approaches, exposing similarities and differences, (4) study the ranking behaviours of approaches alternative to PRP in terms of the kinematics they impose on relevant documents, i. e. by considering the extent and direction of the movements of relevant documents across the ranking recorded when comparing PRP against its alternatives. Our findings show that the effectiveness of the examined ranking approaches strongly depends upon the evaluation context. In the traditional evaluation context of ad-hoc retrieval, PRP is empirically shown to be better than or comparable to alternative ranking approaches. However, when evaluation contexts that account for interdependent document relevance are examined (i. e. when the relevance of a document is assessed also with respect to other retrieved documents, as it is the case in the ersity retrieval scenario), the use of quantum probability theory and thus of qPRP is shown to improve retrieval and ranking effectiveness over the traditional PRP and alternative ranking strategies, such as Maximal Marginal Relevance, Portfolio theory, and Interactive PRP. This work represents a significant step forward regarding the use of quantum theory in information retrieval. It demonstrates that the application of quantum theory to problems within information retrieval can lead to improvements both in modelling power and retrieval effectiveness, allowing the constructions of models that capture the complexity of information retrieval situations. Furthermore, the thesis opens up a number of lines of future research. These include investigating estimations and approximations of quantum interference in qPRP, exploiting complex numbers for the representation of documents and queries, and applying the concepts underlying qPRP to tasks other than document ranking. This dissertation was completed at School of Computing Science, University of Glasgow under the advise of Dr. Leif Azzopardi and Prof. Keith van Rijsbergen. Prof. Norbert Fuhr, Dr. Iadh Ounis, and Dr. John O'Donnell served as dissertation committee members. For the full dissertation, visit: theses.gla.ac.uk/3463.
Publisher: ACM
Date: 24-10-2016
Publisher: ACM
Date: 19-07-2010
Publisher: ACM
Date: 03-04-2017
Publisher: ACM
Date: 06-11-2017
Publisher: ACM
Date: 09-08-2023
Publisher: ACM
Date: 27-02-2023
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Emerald
Date: 24-01-2018
Abstract: A conceptual model describes important factors within a system and how they relate to one another. They are important because they help to identify system changes that can yield the greatest improvement. Within information retrieval (IR), most research is directed towards multi-document retrieval and a multi-interaction IR user scenario. There are few, if any, IR conceptual models supporting minimal or single-interaction IR (siIR) user scenarios, however the need for siIR systems is growing rapidly. The purpose of this paper is to take the first step towards constructing a task-oriented conceptual model and experimental framework to support siIR research. A first principles approach is employed to develop a task-oriented conceptual model, called bridging information retrieval (BIR). This model is contrasted with the concept of relevance, a central factor within IR research. BIR introduces the central concept of bridging information (BI) as the objective of IR systems. BI is the additional information a user requires to complete a task, beyond their innate knowledge. The relationship between BI and relevance is determined. The theoretical basis of BIR is derived axiomatically however the resulting system evaluation model is speculative. The proposed operational framework offers researchers a systematic approach to designing and evaluating siIR systems. This work contributes a novel task-oriented IR conceptual model and evaluation framework, both centred around the concept of BI for siIR. It also contributes a novel search task classification method.
Publisher: ACM
Date: 27-02-2023
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: JMIR Publications Inc.
Date: 07-05-2018
Abstract: nderstandability plays a key role in ensuring that people accessing health information are capable of gaining insights that can assist them with their health concerns and choices. The access to unclear or misleading information has been shown to negatively impact the health decisions of the general public. he aim of this study was to investigate methods to estimate the understandability of health Web pages and use these to improve the retrieval of information for people seeking health advice on the Web. ur investigation considered methods to automatically estimate the understandability of health information in Web pages, and it provided a thorough evaluation of these methods using human assessments as well as an analysis of preprocessing factors affecting understandability estimations and associated pitfalls. Furthermore, lessons learned for estimating Web page understandability were applied to the construction of retrieval methods, with specific attention to retrieving information understandable by the general public. e found that machine learning techniques were more suitable to estimate health Web page understandability than traditional readability formulae, which are often used as guidelines and benchmark by health information providers on the Web (larger difference found for Pearson correlation of .602 using gradient boosting regressor compared with .438 using Simple Measure of Gobbledygook Index with the Conference and Labs of the Evaluation Forum eHealth 2015 collection). he findings reported in this paper are important for specialized search services tailored to support the general public in seeking health advice on the Web, as they document and empirically validate state-of-the-art techniques and settings for this domain application.
Publisher: Springer Science and Business Media LLC
Date: 03-2017
Publisher: Springer International Publishing
Date: 2022
Publisher: ACM
Date: 09-08-2023
Publisher: ACM
Date: 18-07-2023
Publisher: ACM
Date: 27-06-2018
Publisher: Springer Science and Business Media LLC
Date: 13-07-2012
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: JMIR Publications Inc.
Date: 13-11-2018
Abstract: any clinical questions arise during patient encounters that clinicians are unable to answer. An evidence-based medicine approach expects that clinicians will seek and apply the best available evidence to answer clinical questions. One commonly used source of such evidence is scientific literature, such as that available through MEDLINE and PubMed. Clinicians report that 2 key reasons why they do not use search systems to answer questions is that it takes too much time and that they do not expect to find a definitive answer. So, the question remains about how effectively scientific literature search systems support time-pressured clinicians in making better clinical decisions. The results of this study are important because they can help clinicians and health care organizations to better assess their needs with respect to clinical decision support (CDS) systems and evidence sources. The results and data captured will contribute a significant data collection to inform the design of future CDS systems to better meet the needs of time-pressured, practicing clinicians. he purpose of this study is to understand the impact of using a scientific medical literature search system on clinical decision making. Furthermore, to understand the impact of realistic time pressures on clinicians, we vary the search time available to find clinical answers. Finally, we assess the impact of improvements in search system effectiveness on the same clinical decisions. n this study, 96 practicing clinicians and final year medical students are presented with 16 clinical questions which they must answer without access to any external resource. The same questions are then represented to the clinicians however, in this part of the study, the clinicians can use a scientific literature search engine to find evidence to support their answers. The time pressures of practicing clinicians are simulated by limiting answer time to one of 3, 6, or 9 min per question. The correct answer rate is reported both before and after search to assess the impact of the search system and the time constraint. In addition, 2 search systems that use the same user interface, but which vary widely in their search effectiveness, are employed so that the impact of changes in search system effectiveness on clinical decision making can also be assessed. ecruiting began for the study in June 2018. As of the April 4, 2019, there were 69 participants enrolled. The study is expected to close by May 30, 2019, with results to be published in July. ll data collected in this study will be made available at the University of Queensland’s UQ eSpace public data repository. ERR1-10.2196/12803
Publisher: ACM
Date: 05-12-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 08-11-2019
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2018
Publisher: F1000 Research Ltd
Date: 14-08-2018
DOI: 10.12688/F1000RESEARCH.15845.1
Abstract: Biological networks are highly modular and contain a large number of clusters, which are often associated with a specific biological function or disease. Identifying these clusters, or modules, is therefore valuable, but it is not trivial. In this article we propose a recursive method based on the Louvain algorithm for community detection and the PageRank algorithm for authoritativeness weighting in networks. PageRank is used to initialise the weights of nodes in the biological network the Louvain algorithm with the Newman-Girvan criterion for modularity is then applied to the network to identify modules. Any identified module with more than k nodes is further processed by recursively applying PageRank and Louvain, until no module contains more than k nodes (where k is a parameter of the method, no greater than 100). This method is evaluated on a heterogeneous set of six biological networks from the Disease Module Identification DREAM Challenge. Empirical findings suggest that the method is effective in identifying a large number of significant modules, although with substantial variability across restarts of the method.
Publisher: ACM
Date: 05-12-2012
Publisher: Wiley
Date: 18-09-2017
DOI: 10.1002/ASI.23936
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: ACM
Date: 07-11-2014
Publisher: ACM
Date: 29-10-2012
Publisher: ACM
Date: 07-08-2017
Publisher: ACM
Date: 19-07-2010
Publisher: ACM
Date: 27-06-2018
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 09-2008
DOI: 10.1109/DEXA.2008.69
Publisher: Springer International Publishing
Date: 2021
Publisher: Springer International Publishing
Date: 2020
Publisher: ACM
Date: 27-06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 07-2018
DOI: 10.1016/J.ARTMED.2018.04.011
Abstract: Death certificates are an invaluable source of cancer mortality statistics. However, this value can only be realised if accurate, quantitative data can be extracted from certificates-an aim h ered by both the volume and variable quality of certificates written in natural language. This paper proposes an automatic classification system for identifying all cancer related causes of death from death certificates. Detailed features, including terms, n-grams and SNOMED CT concepts were extracted from a collection of 447,336 death certificates. The features were used as input to two different classification sub-systems: a machine learning sub-system using Support Vector Machines (SVMs) and a rule-based sub-system. A fusion sub-system then combines the results from SVMs and rules into a single final classification. A held-out test set was used to evaluate the effectiveness of the classifiers according to precision, recall and F-measure. The system was highly effective at determining the type of cancers for both common cancers (F-measure of 0.85) and rare cancers (F-measure of 0.7). In general, rules performed superior to SVMs however, the fusion method that combined the two was the most effective. The system proposed in this study provides automatic identification and characterisation of cancers from large collections of free-text death certificates. This allows organisations such as Cancer Registries to monitor and report on cancer mortality in a timely and accurate manner. In addition, the methods and findings are generally applicable beyond cancer classification and to other sources of medical text besides death certificates.
Publisher: Springer International Publishing
Date: 2020
Publisher: ACM
Date: 12-09-2016
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer International Publishing
Date: 2018
Publisher: ACM
Date: 07-08-2017
Publisher: ACM
Date: 05-12-2016
Publisher: Oxford University Press
Date: 22-03-2018
DOI: 10.1093/OSO/9780198799603.003.0012
Abstract: This chapter provides a tutorial on how economics can be used to model the interaction between users and systems. Economic theory provides an intuitive and natural way to model Human-Computer Interaction which enables the prediction and explanation of user behaviour. A central tenet of the approach is the utility maximisation paradigm where it is assumed that users seek to maximise their profit/benefit subject to budget and other constraints when interacting with a system. By using such models it is possible to reason about user behaviour and make predictions about how changes to the interface or the users interactions will affect performance and behaviour. In this chapter, we describe and develop several economic models relating to how users search for information. While the ex les are specific to Information Seeking and Retrieval, the techniques employed can be applied more generally to other human-computer interaction scenarios. Therefore, the goal of this chapter is to provide an introduction and overview of how to build economic models of human-computer interaction that generate testable hypotheses regarding user behaviour which can be used to guide design and inform experimentation.
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer International Publishing
Date: 2021
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: ACM
Date: 05-12-2012
Publisher: Scitechnol Biosoft Pvt. Ltd.
Date: 2013
Publisher: Springer International Publishing
Date: 2015
Publisher: ACM
Date: 18-07-2023
Publisher: ACM
Date: 27-06-2018
Publisher: Springer International Publishing
Date: 2016
Publisher: Scitechnol Biosoft Pvt. Ltd.
Date: 2013
Publisher: ACM
Date: 05-12-2016
Publisher: American Society of Civil Engineers (ASCE)
Date: 10-2015
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer International Publishing
Date: 2017
Publisher: No publisher found
Date: 2015
Publisher: ACM
Date: 15-12-2022
Publisher: ACM
Date: 19-07-2009
Publisher: Association for Computing Machinery (ACM)
Date: 10-04-2023
DOI: 10.1145/3570724
Abstract: Pseudo Relevance Feedback (PRF) is known to improve the effectiveness of bag-of-words retrievers. At the same time, deep language models have been shown to outperform traditional bag-of-words rerankers. However, it is unclear how to integrate PRF directly with emergent deep language models. This article addresses this gap by investigating methods for integrating PRF signals with rerankers and dense retrievers based on deep language models. We consider text-based, vector-based and hybrid PRF approaches and investigate different ways of combining and scoring relevance signals. An extensive empirical evaluation was conducted across four different datasets and two task settings (retrieval and ranking). Text-based PRF results show that the use of PRF had a mixed effect on deep rerankers across different datasets. We found that the best effectiveness was achieved when (i) directly concatenating each PRF passage with the query, searching with the new set of queries, and then aggregating the scores (ii) using Borda to aggregate scores from PRF runs. Vector-based PRF results show that the use of PRF enhanced the effectiveness of deep rerankers and dense retrievers over several evaluation metrics. We found that higher effectiveness was achieved when (i) the query retains either the majority or the same weight within the PRF mechanism, and (ii) a shallower PRF signal (i.e., a smaller number of top-ranked passages) was employed, rather than a deeper signal. Our vector-based PRF method is computationally efficient thus, this represents a general PRF method others can use with deep rerankers and dense retrievers.
Publisher: Elsevier BV
Date: 10-2017
DOI: 10.1016/J.IJMEDINF.2017.08.001
Abstract: To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random s ling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random s ling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations.
Publisher: ACM
Date: 17-10-2015
Publisher: ACM
Date: 05-12-2013
Publisher: ACM
Date: 05-12-2013
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: JMIR Publications Inc.
Date: 28-05-2019
DOI: 10.2196/12803
Publisher: JMIR Publications Inc.
Date: 30-01-2019
DOI: 10.2196/10986
Publisher: ACM
Date: 29-10-2012
Publisher: Springer Science and Business Media LLC
Date: 04-08-2022
DOI: 10.1007/S10791-022-09413-Y
Abstract: Online learning to rank (OLTR) aims to learn a ranker directly from implicit feedback derived from users’ interactions, such as clicks. Clicks however are a biased signal: specifically, top-ranked documents are likely to attract more clicks than documents down the ranking (position bias). In this paper, we propose a novel learning algorithm for OLTR that uses reinforcement learning to optimize rankers: Reinforcement Online Learning to Rank (ROLTR). In ROLTR, the gradients of the ranker are estimated based on the rewards assigned to clicked and unclicked documents. In order to de-bias the users’ position bias contained in the reward signals, we introduce unbiased reward shaping functions that exploit inverse propensity scoring for clicked and unclicked documents. The fact that our method can also model unclicked documents provides a further advantage in that less users interactions are required to effectively train a ranker, thus providing gains in efficiency. Empirical evaluation on standard OLTR datasets shows that ROLTR achieves state-of-the-art performance, and provides significantly better user experience than other OLTR approaches. To facilitate the reproducibility of our experiments, we make all experiment code available at elab/OLTR .
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2016
Publisher: ACM
Date: 12-09-2016
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: ACM
Date: 26-11-2014
Start Date: 10-2018
End Date: 12-2021
Amount: $346,446.00
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2021
End Date: 06-2024
Amount: $203,771.00
Funder: Australian Research Council
View Funded Activity