Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 12 - Engineering
- Output identifier
- 7986
- Type
- D - Journal article
- DOI
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10.1016/j.bspc.2017.01.012
- Title of journal
- Biomedical Signal Processing and Control
- Article number
- -
- First page
- 456
- Volume
- 52
- Issue
- -
- ISSN
- 1746-8094
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
5
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Written with an internationally leading team of clinicians, reports a novel methodology revealing the association of IgG4 antibodies with graft rejection in high-risk transplants, a significant finding for transplant medicine: the IgG4 antibody was previously seen as non-harmful. Establishes IgG4 as a new biomarker, allowing access to transplantation for patients otherwise deemed untransplantable. Presented at an invited session in the Houses of Parliament. Established a collaboration, including the plenary address at their international workshop, with One Lambda (USA) who provide assays for antibody testing for new biomarkers. Cited 12x the average article of its age and field (Scopus, 12/20).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -