A Framework for Combining a Motion Atlas with Non-Motion Information to Learn Clinically Useful Biomarkers : Application to Cardiac Resynchronisation Therapy Response Prediction
- Submitting institution
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King's College London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 73637593
- Type
- D - Journal article
- DOI
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10.1016/j.media.2016.10.002
- Title of journal
- Medical Image Analysis
- Article number
- -
- First page
- 669
- Volume
- 35
- Issue
- -
- ISSN
- 1361-8415
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2016
- URL
-
-
- Supplementary information
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- Request cross-referral to
- -
- 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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10
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This collaboration with Imperial College London and clinicians from St. Thomas' Hospital was the first to propose the use of machine learning for making personalised predictions about response to treatment for heart failure. The technique was evaluated on patient data and saves hospitals time/money spent on unnecessary and potentially risky treatments. The paper has proved influential in the emerging crossover field between cardiology and machine learning, including recent larger clinical studies (>1000 patients) that have brought this technology closer to clinical impact (https://doi.org/10.1002/ejhf.1333). The paper led to the successful award of follow-on EPSRC funding (EP/P001009/1, EP/R005516/1).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
