From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support
- Submitting institution
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Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 433
- Type
- D - Journal article
- DOI
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10.1016/j.artmed.2016.01.002
- Title of journal
- Artificial Intelligence in Medicine
- Article number
- -
- First page
- 75
- Volume
- 67
- Issue
- -
- ISSN
- 0933-3657
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2016
- URL
-
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Citation count
- 61
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A rigorous and repeatable method for building effective Bayesian Network models for medical decision support when the input data are 'complex'; i.e., such as data from patient responses to unstructured and incomplete questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses. Work featured in the online magazine “Atlas of Science” (2015), and led to invited talk (Fenton) at 26th conference on Subjective Probability, Utility and Decision Making (2017). This work contributed to the EPSRC 3-year Fellowship award (Constantinou) ""Bayesian-AI"" in 2018 (https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/S001646/1), and to the US Department of Defence award (Marsh) 2019 on Trauma Sciences (https://www.qmul.ac.uk/media/news/2019/smd/us-department-of-defense-awards-1m-to-queen-mary-university-of-london-for-ai-research-on-treating-injured-soldiers.html).
- Author contribution statement
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- Non-English
- No
- English abstract
- -