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Schofield P, Hyatt A, White A, White F, Frydenberg M, Chambers S, Gardiner R, Murphy DG, Cavedon L, Millar J, Richards N, Murphy B, Juraskova I. Co-designing an online treatment decision aid for men with low-risk prostate cancer: Navigate. BJUI Compass 2024; 5:121-141. [PMID: 38179019 PMCID: PMC10764164 DOI: 10.1002/bco2.279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/01/2023] [Indexed: 01/06/2024] Open
Abstract
Objectives To develop an online treatment decision aid (OTDA) to assist patients with low-risk prostate cancer (LRPC) and their partners in making treatment decisions. Patients and methods Navigate, an OTDA for LRPC, was rigorously co-designed by patients with a confirmed diagnosis or at risk of LRPC and their partners, clinicians, researchers and website designers/developers. A theoretical model guided the development process. A mixed methods approach was used incorporating (1) evidence for essential design elements for OTDAs; (2) evidence for treatment options for LRPC; (3) an iterative co-design process involving stakeholder workshops and prototype review; and (4) expert rating using the International Patient Decision Aid Standards (IPDAS). Three co-design workshops with potential users (n = 12) and research and web-design team members (n = 10) were conducted. Results from each workshop informed OTDA modifications to the OTDA for testing in the subsequent workshop. Clinician (n = 6) and consumer (n = 9) feedback on usability and content on the penultimate version was collected. Results The initial workshops identified key content and design features that were incorporated into the draft OTDA, re-workshopped and incorporated into the penultimate OTDA. Expert feedback on usability and content was also incorporated into the final OTDA. The final OTDA was deemed comprehensive, clear and appropriate and met all IPDAS criteria. Conclusion Navigate is an interactive and acceptable OTDA for Australian men with LRPC designed by men for men using a co-design methodology. The effectiveness of Navigate in assisting patient decision-making is currently being assessed in a randomised controlled trial with patients with LRPC and their partners.
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Affiliation(s)
- Penelope Schofield
- Department of PsychologySwinburne University of TechnologyMelbourneVictoriaAustralia
- Health Services Research DepartmentPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of OncologyUniversity of MelbourneParkvilleVictoriaAustralia
| | - Amelia Hyatt
- Health Services Research DepartmentPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of OncologyUniversity of MelbourneParkvilleVictoriaAustralia
| | - Alan White
- Health Services Research DepartmentPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Fiona White
- Health Services Research DepartmentPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Mark Frydenberg
- Department of Urology, Cabrini InstituteCabrini HealthMelbourneVictoriaAustralia
- Department of SurgeryMonash UniversityMelbourneVictoriaAustralia
| | - Suzanne Chambers
- Faculty of Health SciencesAustralian Catholic UniversityBrisbaneQueenslandAustralia
- Faculty of HealthUniversity of Technology SydneySydneyNew South WalesAustralia
- Menzies Health InstituteGriffith UniversityNathanQueenslandAustralia
| | - Robert Gardiner
- School of MedicineUniversity of QueenslandSt LuciaQueenslandAustralia
- Department of UrologyRoyal Brisbane and Women's HospitalHerstonQueenslandAustralia
- Edith Cowan UniversityPerthWestern AustraliaAustralia
| | - Declan G. Murphy
- Sir Peter MacCallum Department of OncologyUniversity of MelbourneParkvilleVictoriaAustralia
- Division of Cancer SurgeryPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Lawrence Cavedon
- School of Computing TechnologiesRMIT UniversityMelbourneVictoriaAustralia
| | - Jeremy Millar
- Radiation Oncology, Alfred HealthMelbourneVictoriaAustralia
- Department of Surgery, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
| | - Natalie Richards
- Health Services Research DepartmentPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| | - Barbara Murphy
- School of Psychological SciencesUniversity of MelbourneParkvilleVictoriaAustralia
| | - Ilona Juraskova
- Centre for Medical Psychology and Evidence‐based Decision‐making (CeMPED), School of PsychologyUniversity of SydneySydneyNew South WalesAustralia
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Sarwar T, Jimeno Yepes AJ, Zhang X, Chan J, Hudson I, Evans S, Cavedon L. Development and validation of retrospective electronic frailty index using operational data of aged care homes. BMC Geriatr 2022; 22:922. [DOI: 10.1186/s12877-022-03616-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/14/2022] [Indexed: 12/02/2022] Open
Abstract
Abstract
Background
Although elderly population is generally frail, it is important to closely monitor their health deterioration to improve the care and support in residential aged care homes (RACs). Currently, the best identification approach is through time-consuming regular geriatric assessments. This study aimed to develop and validate a retrospective electronic frailty index (reFI) to track the health status of people staying at RACs using the daily routine operational data records.
Methods
We have access to patient records from the Royal Freemasons Benevolent Institution RACs (Australia) over the age of 65, spanning 2010 to 2021. The reFI was developed using the cumulative deficit frailty model whose value was calculated as the ratio of number of present frailty deficits to the total possible frailty indicators (32). Frailty categories were defined using population quartiles. 1, 3 and 5-year mortality were used for validation. Survival analysis was performed using Kaplan-Meier estimate. Hazard ratios (HRs) were estimated using Cox regression analyses and the association was assessed using receiver operating characteristic (ROC) curves.
Results
Two thousand five hundred eighty-eight residents were assessed, with an average length of stay of 1.2 ± 2.2 years. The RAC cohort was generally frail with an average reFI of 0.21 ± 0.11. According to the Kaplan-Meier estimate, survival varied significantly across different frailty categories (p < 0.01). The estimated hazard ratios (HRs) were 1.12 (95% CI 1.09–1.15), 1.11 (95% CI 1.07–1.14), and 1.1 (95% CI 1.04–1.17) at 1, 3 and 5 years. The ROC analysis of the reFI for mortality outcome showed an area under the curve (AUC) of ≥0.60 for 1, 3 and 5-year mortality.
Conclusion
A novel reFI was developed using the routine data recorded at RACs. reFI can identify changes in the frailty index over time for elderly people, that could potentially help in creating personalised care plans for addressing their health deterioration.
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Albahem A, Spina D, Scholer F, Cavedon L. Component-based Analysis of Dynamic Search Performance. ACM T INFORM SYST 2022. [DOI: 10.1145/3483237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
In many search scenarios, such as exploratory, comparative, or survey-oriented search, users interact with dynamic search systems to satisfy multi-aspect information needs. These systems utilize different dynamic approaches that exploit various user feedback granularity types. Although studies have provided insights about the role of many components of these systems, they used black-box and isolated experimental setups. Therefore, the effects of these components or their interactions are still not well understood. We address this by following a methodology based on Analysis of Variance (ANOVA). We built a Grid Of Points that consists of systems based on different ways to instantiate three components: initial rankers, dynamic rerankers, and user feedback granularity. Using evaluation scores based on the TREC Dynamic Domain collections, we built several ANOVA models to estimate the effects. We found that (i) although all components significantly affect search effectiveness, the initial ranker has the largest effective size, (ii) the effect sizes of these components vary based on the length of the search session and the used effectiveness metric, and (iii) initial rankers and dynamic rerankers have more prominent effects than user feedback granularity. To improve effectiveness, we recommend improving the quality of initial rankers and dynamic rerankers. This does not require eliciting detailed user feedback, which might be expensive or invasive.
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Vukovic M, Cavedon L, Thangarajah J, Rodriguez S. Performance degrades less under increased workload with the addition of speech control in a dynamic environment. Appl Ergon 2021; 96:103486. [PMID: 34139375 DOI: 10.1016/j.apergo.2021.103486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 05/11/2021] [Accepted: 05/26/2021] [Indexed: 06/12/2023]
Abstract
This research empirically evaluates the introduction of speech to existing keyboard and mouse input modalities in an application used to control aircraft in a simulated, complex and dynamic environment. Task performance and task performance degradation are assessed for three levels of workload. Previous studies have evaluated task performance using these modalities however, only a couple have evaluated task performance under varying workload. Even though speech is a common addition to modern control interfaces, the effect of varying workload on this combination of control modalities has not yet been reported. Thirty-six participants commanded simulated aircraft through generated obstacle courses to reach a Combat Air Patrol (CAP) point while also responding to a secondary task. There were nine conditions that varied the control modality (Keyboard and Mouse (KM), Voice (V), and Keyboard, Mouse and Voice (KMV)), and workload by varying the number of aircraft being controlled (low, medium and high). Results showed that KM outperformed KMV and V for the low and medium workload levels. However, task performance with KMV was found to degrade the least as workload increased. KMV and KM were found to enable significantly more correct responses to the secondary task which was delivered aurally. Participants reported a preference for the combined modalities (KMV), self-assessing that KMV most reduced their workload. This research suggests that the addition of a speech interface to existing keyboard and mouse modalities, for control of aircraft in a simulation, may help manage cognitive load and may assist in controlling more aircraft under higher workloads.
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Affiliation(s)
- Maria Vukovic
- RMIT University, PO Box 2476, Melbourne, VIC, 3001, Australia; Defence Science and Technology Group, 506 Lorimer Street, Fishermans Bend, Melbourne, VIC, 3207, Australia.
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He J, Nguyen DQ, Akhondi SA, Druckenbrodt C, Thorne C, Hoessel R, Afzal Z, Zhai Z, Fang B, Yoshikawa H, Albahem A, Cavedon L, Cohn T, Baldwin T, Verspoor K. ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents. Front Res Metr Anal 2021; 6:654438. [PMID: 33870071 PMCID: PMC8028406 DOI: 10.3389/frma.2021.654438] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 02/24/2021] [Indexed: 11/21/2022] Open
Abstract
Chemical patents represent a valuable source of information about new chemical compounds, which is critical to the drug discovery process. Automated information extraction over chemical patents is, however, a challenging task due to the large volume of existing patents and the complex linguistic properties of chemical patents. The Cheminformatics Elsevier Melbourne University (ChEMU) evaluation lab 2020, part of the Conference and Labs of the Evaluation Forum 2020 (CLEF2020), was introduced to support the development of advanced text mining techniques for chemical patents. The ChEMU 2020 lab proposed two fundamental information extraction tasks focusing on chemical reaction processes described in chemical patents: (1) chemical named entity recognition, requiring identification of essential chemical entities and their roles in chemical reactions, as well as reaction conditions; and (2) event extraction, which aims at identification of event steps relating the entities involved in chemical reactions. The ChEMU 2020 lab received 37 team registrations and 46 runs. Overall, the performance of submissions for these tasks exceeded our expectations, with the top systems outperforming strong baselines. We further show the methods to be robust to variations in sampling of the test data. We provide a detailed overview of the ChEMU 2020 corpus and its annotation, showing that inter-annotator agreement is very strong. We also present the methods adopted by participants, provide a detailed analysis of their performance, and carefully consider the potential impact of data leakage on interpretation of the results. The ChEMU 2020 Lab has shown the viability of automated methods to support information extraction of key information in chemical patents.
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Affiliation(s)
- Jiayuan He
- The University of Melbourne, Parkville, VIC, Australia.,RMIT University, Melbourne, VIC, Australia
| | - Dat Quoc Nguyen
- The University of Melbourne, Parkville, VIC, Australia.,VinAI Research, Hanoi, Vietnam
| | | | | | - Camilo Thorne
- Elsevier Information Systems GmbH, Frankfurt, Germany
| | - Ralph Hoessel
- Elsevier Information Systems GmbH, Frankfurt, Germany
| | | | - Zenan Zhai
- The University of Melbourne, Parkville, VIC, Australia
| | - Biaoyan Fang
- The University of Melbourne, Parkville, VIC, Australia
| | - Hiyori Yoshikawa
- The University of Melbourne, Parkville, VIC, Australia.,Fujitsu Laboratories Ltd., Tokyo, Japan
| | - Ameer Albahem
- The University of Melbourne, Parkville, VIC, Australia.,RMIT University, Melbourne, VIC, Australia
| | | | - Trevor Cohn
- The University of Melbourne, Parkville, VIC, Australia
| | | | - Karin Verspoor
- The University of Melbourne, Parkville, VIC, Australia.,RMIT University, Melbourne, VIC, Australia
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6
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Schofield P, Gough K, Hyatt A, White A, Frydenberg M, Chambers S, Gordon LG, Gardiner R, Murphy DG, Cavedon L, Richards N, Murphy B, Quinn S, Juraskova I. Correction to: Navigate: a study protocol for a randomised controlled trial of an online treatment decision aid for men with low-risk prostate cancer and their partners. Trials 2021; 22:97. [PMID: 33504356 PMCID: PMC7839185 DOI: 10.1186/s13063-021-05070-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Penelope Schofield
- Department of Psychology, Swinburne University of Technology, Melbourne, Victoria, Australia. .,Behavioural Science Unit, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia. .,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia. .,Swinburne University of Technology, John Street, Hawthorn, Australia.
| | - Karla Gough
- Behavioural Science Unit, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Department of Nursing, The University of Melbourne, Parkville, Victoria, Australia
| | - Amelia Hyatt
- Behavioural Science Unit, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Alan White
- Behavioural Science Unit, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Mark Frydenberg
- Department of Urology, Cabrini Institute, Cabrini Health, Malvern, Australia.,Department of Surgery, Monash University, Melbourne, Victoria, Australia
| | - Suzanne Chambers
- Faculty of Health, University of Technology Sydney, Sydney, Australia.,Health and Wellness Institute, Edith Cowan University, Perth, Australia.,Institute for Resilient Regions, University of Southern Queensland, Springfield, Australia.,Menzies Health Institute Queensland, Griffith University, Southport, Australia
| | - Louisa G Gordon
- Population Health Department, Health Economics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,School of Nursing, Queensland University of Technology (QUT), Brisbane, Queensland, Australia.,School of Public Health, University of Queensland, Brisbane, Queensland, Australia
| | - Robert Gardiner
- School of Medicine, University of Queensland, Brisbane, Queensland, Australia.,Department of Urology, Royal Brisbane & Women's Hospital, Herston, Queensland, Australia
| | - Declan G Murphy
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.,Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Lawrence Cavedon
- School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Natalie Richards
- Behavioural Science Unit, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Barbara Murphy
- Department of Psychology, The University of Melbourne, Parkville, Victoria, Australia.,Faculty of Health, Deakin University, Bundoora, Victoria, Australia
| | - Stephen Quinn
- Department of Health Science and Biostatistics, Swinburne University of Technology, Melbourne, Victoria, Australia
| | - Ilona Juraskova
- School of Psychology, Faculty of Science, Centre for Medical Psychology and Evidence-based Decision-making (CeMPED), University of Sydney, Sydney, New South Wales, Australia
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7
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Schofield P, Gough K, Hyatt A, White A, Frydenberg M, Chambers S, Gordon LG, Gardiner R, Murphy DG, Cavedon L, Richards N, Murphy B, Quinn S, Juraskova I. Navigate: a study protocol for a randomised controlled trial of an online treatment decision aid for men with low-risk prostate cancer and their partners. Trials 2021; 22:49. [PMID: 33430950 PMCID: PMC7802237 DOI: 10.1186/s13063-020-04986-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 12/18/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Active surveillance (AS) is the disease management option of choice for low-risk prostate cancer. Despite this, men with low-risk prostate cancer (LRPC) find management decisions distressing and confusing. We developed Navigate, an online decision aid to help men and their partners make management decisions consistent with their values. The aims are to evaluate the impact of Navigate on uptake of AS; decision-making preparedness; decisional conflict, regret and satisfaction; quality of illness communication; and prostate cancer-specific quality of life and anxiety. In addition, the healthcare cost impact, cost-effectiveness and patterns of use of Navigate will be assessed. This paper describes the study protocol. METHODS Three hundred four men and their partners are randomly assigned one-to-one to Navigate or to the control arm. Randomisation is electronically generated and stratified by site. Navigate is an online decision aid that presents up-to-date, unbiased information on LRPC tailored to Australian men and their partners including each management option and potential side-effects, and an interactive values clarification exercise. Participants in the control arm will be directed to the website of Australia's peak national body for prostate cancer. Eligible patients will be men within 3 months of being diagnosed with LRPC, aged 18 years or older, and who are yet to make a treatment decision, who are deemed eligible for AS by their treating clinician and who have Internet access and sufficient English to participate. The primary outcome is self-reported uptake of AS as the first-line management option. Secondary outcomes include self-reported preparedness for decision-making; decisional conflict, regret and satisfaction; quality of illness communication; and prostate cancer-specific quality of life. Uptake of AS 1 month after consent will be determined through patient self-report. Men and their partners will complete study outcome measures before randomisation and 1, 3 and 6 months after study consent. DISCUSSION The Navigate online decision aid has the potential to increase the choice of AS in LRPC, avoiding or delaying unnecessary radical treatments and associated side effects. In addition, Navigate is likely to reduce patients' and partners' confusion and distress in management decision-making and increase their quality of life. TRIAL REGISTRATION Australian and New Zealand Clinical Trial Registry ACTRN12616001665426 . Registered on 2 December 2016. All items from the WHO Trial Registration Data set can be found in this manuscript.
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Affiliation(s)
- Penelope Schofield
- Department of Psychology, Swinburne University of Technology, Melbourne, Victoria, Australia. .,Behavioural Science Unit, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia. .,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia. .,Swinburne University of Technology, John Street, Hawthorn, Australia.
| | - Karla Gough
- Behavioural Science Unit, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Department of Nursing, The University of Melbourne, Parkville, Victoria, Australia
| | - Amelia Hyatt
- Behavioural Science Unit, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Alan White
- Behavioural Science Unit, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Mark Frydenberg
- Department of Urology, Cabrini Institute, Cabrini Health, Malvern, Australia.,Department of Surgery, Monash University, Melbourne, Victoria, Australia
| | - Suzanne Chambers
- Faculty of Health, University of Technology Sydney, Sydney, Australia.,Health and Wellness Institute, Edith Cowan University, Perth, Australia.,Institute for Resilient Regions, University of Southern Queensland, Springfield, Australia.,Menzies Health Institute Queensland, Griffith University, Southport, Australia
| | - Louisa G Gordon
- Population Health Department, Health Economics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,School of Nursing, Queensland University of Technology (QUT), Brisbane, Queensland, Australia.,School of Public Health, University of Queensland, Brisbane, Queensland, Australia
| | - Robert Gardiner
- School of Medicine, University of Queensland, Brisbane, Queensland, Australia.,Department of Urology, Royal Brisbane & Women's Hospital, Herston, Queensland, Australia
| | - Declan G Murphy
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.,Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Lawrence Cavedon
- School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Natalie Richards
- Behavioural Science Unit, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Barbara Murphy
- Department of Psychology, The University of Melbourne, Parkville, Victoria, Australia.,Faculty of Health, Deakin University, Bundoora, Victoria, Australia
| | - Stephen Quinn
- Department of Health Science and Biostatistics, Swinburne University of Technology, Melbourne, Victoria, Australia
| | - Ilona Juraskova
- School of Psychology, Faculty of Science, Centre for Medical Psychology and Evidence-based Decision-making (CeMPED), University of Sydney, Sydney, New South Wales, Australia
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Fayek HM, Cavedon L, Wu HR. Progressive learning: A deep learning framework for continual learning. Neural Netw 2020; 128:345-357. [PMID: 32470799 DOI: 10.1016/j.neunet.2020.05.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Revised: 03/21/2020] [Accepted: 05/11/2020] [Indexed: 11/29/2022]
Abstract
Continual learning is the ability of a learning system to solve new tasks by utilizing previously acquired knowledge from learning and performing prior tasks without having significant adverse effects on the acquired prior knowledge. Continual learning is key to advancing machine learning and artificial intelligence. Progressive learning is a deep learning framework for continual learning that comprises three procedures: curriculum, progression, and pruning. The curriculum procedure is used to actively select a task to learn from a set of candidate tasks. The progression procedure is used to grow the capacity of the model by adding new parameters that leverage parameters learned in prior tasks, while learning from data available for the new task at hand, without being susceptible to catastrophic forgetting. The pruning procedure is used to counteract the growth in the number of parameters as further tasks are learned, as well as to mitigate negative forward transfer, in which prior knowledge unrelated to the task at hand may interfere and worsen performance. Progressive learning is evaluated on a number of supervised classification tasks in the image recognition and speech recognition domains to demonstrate its advantages compared with baseline methods. It is shown that, when tasks are related, progressive learning leads to faster learning that converges to better generalization performance using a smaller number of dedicated parameters.
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Affiliation(s)
- Haytham M Fayek
- School of Engineering, RMIT University, Melbourne VIC 3001, Australia.
| | - Lawrence Cavedon
- School of Science, RMIT University, Melbourne VIC 3001, Australia
| | - Hong Ren Wu
- School of Engineering, RMIT University, Melbourne VIC 3001, Australia
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9
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Pereira-Salgado A, Westwood JA, Russell L, Ugalde A, Ortlepp B, Seymour JF, Butow P, Cavedon L, Ong K, Aranda S, Breen S, Kirsa S, Dunlevie A, Schofield P. Mobile Health Intervention to Increase Oral Cancer Therapy Adherence in Patients With Chronic Myeloid Leukemia (The REMIND System): Clinical Feasibility and Acceptability Assessment. JMIR Mhealth Uhealth 2017; 5:e184. [PMID: 29212628 PMCID: PMC5738545 DOI: 10.2196/mhealth.8349] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 10/04/2017] [Indexed: 01/01/2023] Open
Abstract
Background Optimal dosing of oral tyrosine kinase inhibitor therapy is critical to treatment success and survival of patients with chronic myeloid leukemia (CML). Drug intolerance secondary to toxicities and nonadherence are significant factors in treatment failure. Objective The objective of this study was to develop and pilot-test the clinical feasibility and acceptability of a mobile health system (REMIND) to increase oral drug adherence and patient symptom self-management among people with CML (chronic phase). Methods A multifaceted intervention was iteratively developed using the intervention development framework by Schofield and Chambers, consisting of defining the patient problem and iteratively refining the intervention. The clinical feasibility and acceptability were examined via patient and intervention nurse interviews, which were audiotaped, transcribed, and deductively content analyzed. Results The intervention comprised 2 synergistically operating elements: (1) daily medication reminders and routine assessment of side effects with evidence-based self-care advice delivered in real time and (2) question prompt list (QPL) questions and routinely collected individual patient adherence and side effect profile data used to shape nurses’ consultations, which employed motivational interviewing to support adoption of self-management behaviors. A total of 4 consultations and daily alerts and advice were delivered over 10 weeks. In total, 58% (10/17) of patients and 2 nurses participated in the pilot study. Patients reported several benefits of the intervention: help in establishing medication routines, resolution of symptom uncertainty, increased awareness of self-care, and informed decision making. Nurses also endorsed the intervention: it assisted in establishing pill-taking routines and patients developing effective solutions to adherence challenges. Conclusions The REMIND system with nurse support was usable and acceptable to both patients and nurses. It has the potential to improve adherence and side-effect management and should be further evaluated.
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Affiliation(s)
- Amanda Pereira-Salgado
- Centre for Nursing Research, Cabrini Institute, Malvern, Victoria, Australia.,Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Jennifer A Westwood
- Department of Cancer Experiences Research, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia
| | - Lahiru Russell
- School of Nursing and Midwifery, Faculty of Health, Deakin University, Geelong, Victoria, Australia
| | - Anna Ugalde
- School of Nursing and Midwifery, Faculty of Health, Deakin University, Geelong, Victoria, Australia
| | - Bronwen Ortlepp
- Department of Haematology, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - John F Seymour
- Department of Haematology, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, Victoria, Australia
| | - Phyllis Butow
- School of Psychology, The University of Sydney, Sydney, New South Wales, Australia
| | - Lawrence Cavedon
- School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Kevin Ong
- School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Sanchia Aranda
- Department of Cancer Experiences Research, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia.,Cancer Council Australia, Sydney, New South Wales, Australia
| | - Sibilah Breen
- Department of Cancer Experiences Research, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia.,Public Health Group, Stroke Division, The Florey Institute of Neuroscience and Mental Health, Heidelberg , Victoria, Australia
| | - Suzanne Kirsa
- Pharmacy Department, Monash Health, Clayton, Victoria, Australia.,Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Andrew Dunlevie
- Department of Cancer Experiences Research, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia
| | - Penelope Schofield
- Department of Cancer Experiences Research, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, Victoria, Australia.,Department of Psychology, School of Health Sciences, Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, Victoria, Australia
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10
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Affiliation(s)
- Damiano Spina
- School of Science, RMIT University; GPO Box 2476, Melbourne VIC 3001 Australia
| | - Johanne R. Trippas
- School of Science, RMIT University; GPO Box 2476, Melbourne VIC 3001 Australia
| | - Lawrence Cavedon
- School of Science, RMIT University; GPO Box 2476, Melbourne VIC 3001 Australia
| | - Mark Sanderson
- School of Science, RMIT University; GPO Box 2476, Melbourne VIC 3001 Australia
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11
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Fayek HM, Lech M, Cavedon L. Evaluating deep learning architectures for Speech Emotion Recognition. Neural Netw 2017; 92:60-68. [DOI: 10.1016/j.neunet.2017.02.013] [Citation(s) in RCA: 174] [Impact Index Per Article: 24.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 02/12/2017] [Accepted: 02/13/2017] [Indexed: 10/19/2022]
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12
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Pitson G, Banks P, Cavedon L, Verspoor K. Developing a Manually Annotated Corpus of Clinical Letters for Breast Cancer Patients on Routine Follow-Up. Stud Health Technol Inform 2017; 235:196-200. [PMID: 28423782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This paper introduces the annotation schema and annotation process for a corpus of clinical letters describing the disease course and treatment of oestrogen receptor positive breast cancer patients, after completion of primary surgery and radiotherapy treatment. Concepts related to therapy, clinical signs, and recurrence, as well as relationships linking these, are identified and annotated in 200 letters. This corpus will provide the basis for development of natural language processing tools for automatic extraction of key clinical factors from such letters.
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Kocbek S, Cavedon L, Martinez D, Bain C, Manus CM, Haffari G, Zukerman I, Verspoor K. Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources. J Biomed Inform 2016; 64:158-167. [PMID: 27742349 DOI: 10.1016/j.jbi.2016.10.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 08/20/2016] [Accepted: 10/10/2016] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Text and data mining play an important role in obtaining insights from Health and Hospital Information Systems. This paper presents a text mining system for detecting admissions marked as positive for several diseases: Lung Cancer, Breast Cancer, Colon Cancer, Secondary Malignant Neoplasm of Respiratory and Digestive Organs, Multiple Myeloma and Malignant Plasma Cell Neoplasms, Pneumonia, and Pulmonary Embolism. We specifically examine the effect of linking multiple data sources on text classification performance. METHODS Support Vector Machine classifiers are built for eight data source combinations, and evaluated using the metrics of Precision, Recall and F-Score. Sub-sampling techniques are used to address unbalanced datasets of medical records. We use radiology reports as an initial data source and add other sources, such as pathology reports and patient and hospital admission data, in order to assess the research question regarding the impact of the value of multiple data sources. Statistical significance is measured using the Wilcoxon signed-rank test. A second set of experiments explores aspects of the system in greater depth, focusing on Lung Cancer. We explore the impact of feature selection; analyse the learning curve; examine the effect of restricting admissions to only those containing reports from all data sources; and examine the impact of reducing the sub-sampling. These experiments provide better understanding of how to best apply text classification in the context of imbalanced data of variable completeness. RESULTS Radiology questions plus patient and hospital admission data contribute valuable information for detecting most of the diseases, significantly improving performance when added to radiology reports alone or to the combination of radiology and pathology reports. CONCLUSION Overall, linking data sources significantly improved classification performance for all the diseases examined. However, there is no single approach that suits all scenarios; the choice of the most effective combination of data sources depends on the specific disease to be classified.
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Affiliation(s)
- Simon Kocbek
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia; School of Science, RMIT University, Melbourne, Australia; Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia.
| | | | - David Martinez
- Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia
| | - Christopher Bain
- Mercy Health, Heidelberg, Australia; Faculty of Information Technology, Monash University, Clayton, Australia
| | - Chris Mac Manus
- Health Informatics Department, Alfred Hospital, Melbourne, Australia; Now with OzeScribe, Melbourne, Australia
| | - Gholamreza Haffari
- Faculty of Information Technology, Monash University, Clayton, Australia
| | - Ingrid Zukerman
- Faculty of Information Technology, Monash University, Clayton, Australia
| | - Karin Verspoor
- Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia
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Affiliation(s)
- Daniel Macias-Galindo
- School of Computer Science & IT; RMIT University; 124 La Trobe St Melbourne VIC 3000 Australia
| | - Lawrence Cavedon
- School of Computer Science & IT; RMIT University; 124 La Trobe St Melbourne VIC 3000 Australia
| | - John Thangarajah
- School of Computer Science & IT; RMIT University; 124 La Trobe St Melbourne VIC 3000 Australia
| | - Wilson Wong
- School of Computer Science & IT; RMIT University; 124 La Trobe St Melbourne VIC 3000 Australia
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Martinez D, Ananda-Rajah MR, Suominen H, Slavin MA, Thursky KA, Cavedon L. Automatic detection of patients with invasive fungal disease from free-text computed tomography (CT) scans. J Biomed Inform 2014; 53:251-60. [PMID: 25460203 DOI: 10.1016/j.jbi.2014.11.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2014] [Revised: 11/05/2014] [Accepted: 11/17/2014] [Indexed: 12/21/2022]
Abstract
BACKGROUND Invasive fungal diseases (IFDs) are associated with considerable health and economic costs. Surveillance of the more diagnostically challenging invasive fungal diseases, specifically of the sino-pulmonary system, is not feasible for many hospitals because case finding is a costly and labour intensive exercise. We developed text classifiers for detecting such IFDs from free-text radiology (CT) reports, using machine-learning techniques. METHOD We obtained free-text reports of CT scans performed over a specific hospitalisation period (2003-2011), for 264 IFD and 289 control patients from three tertiary hospitals. We analysed IFD evidence at patient, report, and sentence levels. Three infectious disease experts annotated the reports of 73 IFD-positive patients for language suggestive of IFD at sentence level, and graded the sentences as to whether they suggested or excluded the presence of IFD. Reliable agreement between annotators was obtained and this was used as training data for our classifiers. We tested a variety of Machine Learning (ML), rule based, and hybrid systems, with feature types including bags of words, bags of phrases, and bags of concepts, as well as report-level structured features. Evaluation was carried out over a robust framework with separate Development and Held-Out datasets. RESULTS The best systems (using Support Vector Machines) achieved very high recall at report- and patient-levels over unseen data: 95% and 100% respectively. Precision at report-level over held-out data was 71%; however, most of the associated false-positive reports (53%) belonged to patients who had a previous positive report appropriately flagged by the classifier, reducing negative impact in practice. CONCLUSIONS Our machine learning application holds the potential for developing systematic IFD surveillance systems for hospital populations.
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Affiliation(s)
| | | | - Hanna Suominen
- NICTA and The Australian National University, Canberra, Australia; University of Canberra, Canberra, Australia; University of Turku, Finland.
| | - Monica A Slavin
- Victorian Infectious Diseases Service, Royal Melbourne Hospital, Peter MacCallum Cancer Institute, Australia; Infectious Diseases Department, Peter MacCallum Cancer Institute, Australia.
| | - Karin A Thursky
- Victorian Infectious Diseases Service, Royal Melbourne Hospital, Peter MacCallum Cancer Institute, Australia; Infectious Diseases Department, Peter MacCallum Cancer Institute, Australia.
| | - Lawrence Cavedon
- School of Computer Science and IT, RMIT University, Melbourne, Australia.
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Martinez D, Pitson G, MacKinlay A, Cavedon L. Cross-hospital portability of information extraction of cancer staging information. Artif Intell Med 2014; 62:11-21. [PMID: 25001545 DOI: 10.1016/j.artmed.2014.06.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2013] [Revised: 06/14/2014] [Accepted: 06/16/2014] [Indexed: 11/19/2022]
Abstract
OBJECTIVE We address the task of extracting information from free-text pathology reports, focusing on staging information encoded by the TNM (tumour-node-metastases) and ACPS (Australian clinico-pathological stage) systems. Staging information is critical for diagnosing the extent of cancer in a patient and for planning individualised treatment. Extracting such information into more structured form saves time, improves reporting, and underpins the potential for automated decision support. METHODS AND MATERIAL We investigate the portability of a text mining model constructed from records from one health centre, by applying it directly to the extraction task over a set of records from a different health centre, with different reporting narrative characteristics. Other than a simple normalisation step on features associated with target labels, we apply the models from one system directly to the other. RESULTS The best F-scores for in-hospital experiments are 81%, 85%, and 94% (for staging T, N, and M respectively), while best cross-hospital F-scores reach 84%, 81%, and 91% for the same respective categories. CONCLUSIONS Our performance results compare favourably to the best levels reported in the literature, and--most relevant to our aim here--the cross-corpus results demonstrate the portability of the models we developed.
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Affiliation(s)
- David Martinez
- Department of Computing and Information Systems, The University of Melbourne, Doug McDonell Building, Parkville, 3010 VIC, Australia.
| | - Graham Pitson
- Barwon Health, Geelong Hospital, 1/75 Bellerine Street, Geelong, 3220 VIC, Australia
| | - Andrew MacKinlay
- Department of Computing and Information Systems, The University of Melbourne, Doug McDonell Building, Parkville, 3010 VIC, Australia
| | - Lawrence Cavedon
- School of Computer Science and IT, RMIT University, 124 Latrobe St, Melbourne, 3000 VIC, Australia
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Verspoor K, Jimeno Yepes A, Cavedon L, McIntosh T, Herten-Crabb A, Thomas Z, Plazzer JP. Annotating the biomedical literature for the human variome. Database (Oxford) 2013; 2013:bat019. [PMID: 23584833 PMCID: PMC3676157 DOI: 10.1093/database/bat019] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This article introduces the Variome Annotation Schema, a schema that
aims to capture the core concepts and relations relevant to cataloguing and interpreting
human genetic variation and its relationship to disease, as described in the published
literature. The schema was inspired by the needs of the database curators of the
International Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is
intended to have application to genetic variation information in a range of diseases. The
schema has been applied to a small corpus of full text journal publications on the subject
of inherited colorectal cancer. We show that the inter-annotator agreement on annotation
of this corpus ranges from 0.78 to 0.95 F-score across different entity
types when exact matching is measured, and improves to a minimum F-score
of 0.87 when boundary matching is relaxed. Relations show more variability in agreement,
but several are reliable, with the highest, cohort-has-size, reaching
0.90 F-score. We also explore the relevance of the schema to the InSiGHT
database curation process. The schema and the corpus represent an important new resource
for the development of text mining solutions that address relationships among patient
cohorts, disease and genetic variation, and therefore, we also discuss the role text
mining might play in the curation of information related to the human variome. The corpus
is available at http://opennicta.com/home/health/variome.
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Affiliation(s)
- Karin Verspoor
- National ICT Australia (NICTA), Victoria Research Laboratory, Level 2, Building 193, The University of Melbourne, Parkville VIC 3010, Australia.
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Abstract
AIM Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automatically annotate sentences in medical abstracts with these labels. METHOD We constructed a corpus of 1,000 medical abstracts annotated by hand with specified medical categories (e.g. Intervention, Outcome). We explored the use of various features based on lexical, semantic, structural, and sequential information in the data, using Conditional Random Fields (CRF) for classification. RESULTS For the classification tasks over all labels, our systems achieved micro-averaged f-scores of 80.9% and 66.9% over datasets of structured and unstructured abstracts respectively, using sequential features. In labeling only the key sentences, our systems produced f-scores of 89.3% and 74.0% over structured and unstructured abstracts respectively, using the same sequential features. The results over an external dataset were lower (f-scores of 63.1% for all labels, and 83.8% for key sentences). CONCLUSIONS Of the features we used, the best for classifying any given sentence in an abstract were based on unigrams, section headings, and sequential information from preceding sentences. These features resulted in improved performance over a simple bag-of-words approach, and outperformed feature sets used in previous work.
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Affiliation(s)
- Su Nam Kim
- NICTA VRL, The University of Melbourne, 3010, Australia
- Department of Computer Science and Software Engineering, The University of Melbourne, 3010, Australia
| | - David Martinez
- NICTA VRL, The University of Melbourne, 3010, Australia
- Department of Computer Science and Software Engineering, The University of Melbourne, 3010, Australia
| | - Lawrence Cavedon
- NICTA VRL, The University of Melbourne, 3010, Australia
- Department of Computer Science and Software Engineering, The University of Melbourne, 3010, Australia
- School of Computer Science and IT, RMIT University, Melbourne 3000, Australia
| | - Lars Yencken
- NICTA VRL, The University of Melbourne, 3010, Australia
- Department of Computer Science and Software Engineering, The University of Melbourne, 3010, Australia
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Karimi S, Pohl S, Scholer F, Cavedon L, Zobel J. Boolean versus ranked querying for biomedical systematic reviews. BMC Med Inform Decis Mak 2010; 10:58. [PMID: 20937152 PMCID: PMC2966450 DOI: 10.1186/1472-6947-10-58] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2010] [Accepted: 10/12/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The process of constructing a systematic review, a document that compiles the published evidence pertaining to a specified medical topic, is intensely time-consuming, often taking a team of researchers over a year, with the identification of relevant published research comprising a substantial portion of the effort. The standard paradigm for this information-seeking task is to use Boolean search; however, this leaves the user(s) the requirement of examining every returned result. Further, our experience is that effective Boolean queries for this specific task are extremely difficult to formulate and typically require multiple iterations of refinement before being finalized. METHODS We explore the effectiveness of using ranked retrieval as compared to Boolean querying for the purpose of constructing a systematic review. We conduct a series of experiments involving ranked retrieval, using queries defined methodologically, in an effort to understand the practicalities of incorporating ranked retrieval into the systematic search task. RESULTS Our results show that ranked retrieval by itself is not viable for this search task requiring high recall. However, we describe a refinement of the standard Boolean search process and show that ranking within a Boolean result set can improve the overall search performance by providing early indication of the quality of the results, thereby speeding up the iterative query-refinement process. CONCLUSIONS Outcomes of experiments suggest that an interactive query-development process using a hybrid ranked and Boolean retrieval system has the potential for significant time-savings over the current search process in the systematic reviewing.
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Affiliation(s)
- Sarvnaz Karimi
- NICTA, Dept. of Computer Science and Software Engineering, The University of Melbourne, Melbourne, Victoria 3010, Australia.
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Kaput J, Cotton RGH, Hardman L, Watson M, Al Aqeel AI, Al-Aama JY, Al-Mulla F, Alonso S, Aretz S, Auerbach AD, Bapat B, Bernstein IT, Bhak J, Bleoo SL, Blöcker H, Brenner SE, Burn J, Bustamante M, Calzone R, Cambon-Thomsen A, Cargill M, Carrera P, Cavedon L, Cho YS, Chung YJ, Claustres M, Cutting G, Dalgleish R, den Dunnen JT, Díaz C, Dobrowolski S, dos Santos MRN, Ekong R, Flanagan SB, Flicek P, Furukawa Y, Genuardi M, Ghang H, Golubenko MV, Greenblatt MS, Hamosh A, Hancock JM, Hardison R, Harrison TM, Hoffmann R, Horaitis R, Howard HJ, Barash CI, Izagirre N, Jung J, Kojima T, Laradi S, Lee YS, Lee JY, Gil-da-Silva-Lopes VL, Macrae FA, Maglott D, Marafie MJ, Marsh SGE, Matsubara Y, Messiaen LM, Möslein G, Netea MG, Norton ML, Oefner PJ, Oetting WS, O'Leary JC, de Ramirez AMO, Paalman MH, Parboosingh J, Patrinos GP, Perozzi G, Phillips IR, Povey S, Prasad S, Qi M, Quin DJ, Ramesar RS, Richards CS, Savige J, Scheible DG, Scott RJ, Seminara D, Shephard EA, Sijmons RH, Smith TD, Sobrido MJ, Tanaka T, Tavtigian SV, Taylor GR, Teague J, Töpel T, Ullman-Cullere M, Utsunomiya J, van Kranen HJ, Vihinen M, Webb E, Weber TK, Yeager M, Yeom YI, Yim SH, Yoo HS. Planning the human variome project: the Spain report. Hum Mutat 2009; 30:496-510. [PMID: 19306394 PMCID: PMC5879779 DOI: 10.1002/humu.20972] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The remarkable progress in characterizing the human genome sequence, exemplified by the Human Genome Project and the HapMap Consortium, has led to the perception that knowledge and the tools (e.g., microarrays) are sufficient for many if not most biomedical research efforts. A large amount of data from diverse studies proves this perception inaccurate at best, and at worst, an impediment for further efforts to characterize the variation in the human genome. Because variation in genotype and environment are the fundamental basis to understand phenotypic variability and heritability at the population level, identifying the range of human genetic variation is crucial to the development of personalized nutrition and medicine. The Human Variome Project (HVP; http://www.humanvariomeproject.org/) was proposed initially to systematically collect mutations that cause human disease and create a cyber infrastructure to link locus specific databases (LSDB). We report here the discussions and recommendations from the 2008 HVP planning meeting held in San Feliu de Guixols, Spain, in May 2008.
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Affiliation(s)
- Jim Kaput
- Division of Personalised Nutrition and Medicine, FDA/National Center for Toxicological Research, Jefferson, Arkansas 72079, USA.
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