2023
Dretzke, Janine; Lorenc, Ava; Adriano, Ada; Herd, Clare; Mehanna, Hisham; Nankivell, Paul; Moore, David J.; Karwath, Andreas; Main, Barry; Firth, Charlotte; Gaunt, Claire; Greaves, Colin; Watson, Eila; Gkoutos, Georgios; Ozakinci, Gozde; Wolstenholme, Jane; Brett, Jo; Duda, Joan; Matheson, Lauren; Cherrill, Louise‐Rae; Calvert, Melanie; Kiely, Philip; Gaunt, Piers; Chernbumroong, Saisakul; Mittal, Saloni; Thomas, Steve; Winter, Stuart; Wong, Wai Lup; Team, PETNECK2 Research
Systematic review of patients’ and healthcare professionals’ views on patient‐initiated follow‐up in treated cancer patients Journal Article
In: Cancer Medicine, 2023, ISSN: 2045-7634.
Abstract | Links | BibTeX | Tags: cancer, health data science, medicine, systematic review
@article{9b13f3c6d25842a9bd3efe7343c24a41,
title = {Systematic review of patients’ and healthcare professionals’ views on patient‐initiated follow‐up in treated cancer patients},
author = {Janine Dretzke and Ava Lorenc and Ada Adriano and Clare Herd and Hisham Mehanna and Paul Nankivell and David J. Moore and Andreas Karwath and Barry Main and Charlotte Firth and Claire Gaunt and Colin Greaves and Eila Watson and Georgios Gkoutos and Gozde Ozakinci and Jane Wolstenholme and Jo Brett and Joan Duda and Lauren Matheson and Louise‐Rae Cherrill and Melanie Calvert and Philip Kiely and Piers Gaunt and Saisakul Chernbumroong and Saloni Mittal and Steve Thomas and Stuart Winter and Wai Lup Wong and PETNECK2 Research Team },
doi = {10.1002/cam4.6243},
issn = {2045-7634},
year = {2023},
date = {2023-06-16},
urldate = {2023-06-16},
journal = {Cancer Medicine},
publisher = {John Wiley & Sons},
abstract = {Background: Current follow‐up models in cancer are seen to be unsustainable and inflexible, and there is growing interest in alternative models, such as patient‐initiated follow‐up (PIFU). It is therefore important to understand whether PIFU is acceptable to patients and healthcare professionals (HCPs). Methods: Standard systematic review methodology aimed at limiting bias was used for study identification (to January 2022), selection and data extraction. Thematic synthesis was undertaken for qualitative data, and survey findings were tabulated and described. Results: Nine qualitative studies and 22 surveys were included, mainly in breast and endometrial cancer. Women treated for breast or endometrial cancer and HCPs were mostly supportive of PIFU. Facilitators for PIFU included convenience, control over own health and avoidance of anxiety‐inducing clinic appointments. Barriers included loss of reassurance from scheduled visits and lack of confidence in self‐management. HCPs were supportive of PIFU but concerned about resistance to change, unsuitability of PIFU for some patients and costs. Conclusion: PIFU is viewed mostly positively by women treated for breast or endometrial cancer, and by HCPs, but further evidence is needed from a wider range of cancers, men, and more representative samples. A protocol was registered with PROSPERO (CRD42020181412).},
keywords = {cancer, health data science, medicine, systematic review},
pubstate = {published},
tppubtype = {article}
}
Taib, Bilal Gani; Karwath, Andreas; Wensley, K.; Minku, L.; Gkoutos, G. V.; Moiemen, N.
Artificial intelligence in the management and treatment of burns: A systematic review and meta-analyses Journal Article
In: Journal of Plastic, Reconstructive & Aesthetic Surgery, vol. 77, pp. 133–161, 2023.
Links | BibTeX | Tags: burns, health data science, medicine, systematic review
@article{Taib_2023,
title = {Artificial intelligence in the management and treatment of burns: A systematic review and meta-analyses},
author = {Bilal Gani Taib and Andreas Karwath and K. Wensley and L. Minku and G. V. Gkoutos and N. Moiemen},
url = {https://doi.org/10.1016%2Fj.bjps.2022.11.049},
doi = {10.1016/j.bjps.2022.11.049},
year = {2023},
date = {2023-02-01},
urldate = {2023-02-01},
journal = {Journal of Plastic, Reconstructive & Aesthetic Surgery},
volume = {77},
pages = {133--161},
publisher = {Elsevier BV},
keywords = {burns, health data science, medicine, systematic review},
pubstate = {published},
tppubtype = {article}
}
2022
Manley, Susan Elisabeth; Karwath, Andreas; Williams, John; Nightingale, Peter; Webber, Jonathan; Raghavan, Rajeev; Barratt, Alison; Webster, Craig; Round, Rachel; Stratton, Irene; Gkoutos, Georgios; Roberts, Graham; Mostafa, Samiul; Ghosh, Sandip
Use of HbA1c for new diagnosis of diabetes in those with hyperglycaemia on admission to or attendance at hospital urgently requires research Journal Article
In: British Journal of Diabetes, vol. 22, no. 2, pp. 95–104, 2022.
Links | BibTeX | Tags: diabetes, EHR, health data science, medicine
@article{Manley_2022,
title = {Use of HbA1c for new diagnosis of diabetes in those with hyperglycaemia on admission to or attendance at hospital urgently requires research},
author = {Susan Elisabeth Manley and Andreas Karwath and John Williams and Peter Nightingale and Jonathan Webber and Rajeev Raghavan and Alison Barratt and Craig Webster and Rachel Round and Irene Stratton and Georgios Gkoutos and Graham Roberts and Samiul Mostafa and Sandip Ghosh},
url = {https://doi.org/10.15277%2Fbjd.2022.386},
doi = {10.15277/bjd.2022.386},
year = {2022},
date = {2022-12-01},
urldate = {2022-12-01},
journal = {British Journal of Diabetes},
volume = {22},
number = {2},
pages = {95--104},
publisher = {ABCD Diabetes Care, Ltd.},
keywords = {diabetes, EHR, health data science, medicine},
pubstate = {published},
tppubtype = {article}
}
Williams, John A.; Karwath, Andreas; Round, Rachel A.; Stratton, Irene M.; Ghosh, Sandip; Mostafa, Samiul; Roberts, Graham; Webber, Jonathan; Gkoutos, Georgios; Manley, Susan E.
133-LB: Relationship of HbA1c and Glucose by Ethnicity in UK Biobank Journal Article
In: Diabetes, vol. 71, no. Supplement_1, 2022.
Links | BibTeX | Tags: diabetes, EHR, health data science, medicine, UKBiobank
@article{WILLIAMS_2022,
title = {133-LB: Relationship of HbA1c and Glucose by Ethnicity in UK Biobank},
author = {John A. Williams and Andreas Karwath and Rachel A. Round and Irene M. Stratton and Sandip Ghosh and Samiul Mostafa and Graham Roberts and Jonathan Webber and Georgios Gkoutos and Susan E. Manley},
url = {https://doi.org/10.2337%2Fdb22-133-lb},
doi = {10.2337/db22-133-lb},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
journal = {Diabetes},
volume = {71},
number = {Supplement_1},
publisher = {American Diabetes Association},
keywords = {diabetes, EHR, health data science, medicine, UKBiobank},
pubstate = {published},
tppubtype = {article}
}
Karwath, Andreas; Williams, John A.; Round, Rachel A.; Stratton, Irene M.; Gkoutos, Georgios; Mostafa, Samiul; Roberts, Graham; Webber, Jonathan; Manley, Susan E.
973-P: By How Much Does Red Blood Cell Status Affect the Accuracy of HbA1c? Journal Article
In: Diabetes, vol. 71, no. Supplement_1, 2022.
Links | BibTeX | Tags: diabetes, EHR, health data science, medicine
@article{KARWATH_2022,
title = {973-P: By How Much Does Red Blood Cell Status Affect the Accuracy of HbA1c?},
author = {Andreas Karwath and John A. Williams and Rachel A. Round and Irene M. Stratton and Georgios Gkoutos and Samiul Mostafa and Graham Roberts and Jonathan Webber and Susan E. Manley},
url = {https://doi.org/10.2337%2Fdb22-973-p},
doi = {10.2337/db22-973-p},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
journal = {Diabetes},
volume = {71},
number = {Supplement_1},
publisher = {American Diabetes Association},
keywords = {diabetes, EHR, health data science, medicine},
pubstate = {published},
tppubtype = {article}
}
Lorenc, A; Wells, M; Fulton-Lieuw, T; Nankivell, P; Mehanna, H; Jepson, M; Karwath, Andreas; Main, B; Firth, C; Gaunt, C; Greaves, C; Moore, D; Watson, E; Gkoutos, G; Ozakinci, G; Wolstenholme, J; Dretzke, J; Brett, J; Duda, J; Matheson, L; Cherrill, L -R; Calvert, M; Kiely, P; Gaunt, P; Chernbumroong, S; Mittal, S.; Thomas, S.; Winter, S.; Wong, W.
Clinicians' Views of Patient-initiated Follow-up in Head and Neck Cancer: a Qualitative Study to Inform the PETNECK2 Trial Journal Article
In: Clinical Oncology, vol. 34, no. 4, pp. 230–240, 2022.
Links | BibTeX | Tags: cancer, medicine
@article{Lorenc_2022,
title = {Clinicians' Views of Patient-initiated Follow-up in Head and Neck Cancer: a Qualitative Study to Inform the PETNECK2 Trial},
author = {A Lorenc and M Wells and T Fulton-Lieuw and P Nankivell and H Mehanna and M Jepson and Andreas Karwath and B Main and C Firth and C Gaunt and C Greaves and D Moore and E Watson and G Gkoutos and G Ozakinci and J Wolstenholme and J Dretzke and J Brett and J Duda and L Matheson and L -R Cherrill and M Calvert and P Kiely and P Gaunt and S Chernbumroong and S. Mittal and S. Thomas and S. Winter and W. Wong},
url = {https://doi.org/10.1016%2Fj.clon.2021.11.010},
doi = {10.1016/j.clon.2021.11.010},
year = {2022},
date = {2022-04-01},
urldate = {2022-04-01},
journal = {Clinical Oncology},
volume = {34},
number = {4},
pages = {230--240},
publisher = {Elsevier BV},
keywords = {cancer, medicine},
pubstate = {published},
tppubtype = {article}
}
Williams, John; Burgess, Stephen; Suckling, John; Lalousis, Paris Alexandros; Batool, Fatima; Griffiths, Lowri; Palmer, Edward; Karwath, Andreas; Barsky, Andrey; Gkoutos, Georgios; Wood, Stephen; Barnes, Nicholas; David, Anthony S; Donohoe, Gary; Neill, Joanna; Deakin, Bill; Khandaker, Golam; Upthegrove, Rachel; collaboration, PIMS
Inflammation and brain structure in schizophrenia and other neuropsychiatric disorders: A Mendelian randomization study Journal Article
In: JAMA psychiatry, vol. 2022, pp. 1–11, 2022, ISSN: 2168-622X.
Abstract | Links | BibTeX | Tags: artificial intelligence, health data science, medicine
@article{44ac2137c0fa4666839a213d0fc6175c,
title = {Inflammation and brain structure in schizophrenia and other neuropsychiatric disorders: A Mendelian randomization study},
author = {John Williams and Stephen Burgess and John Suckling and Paris Alexandros Lalousis and Fatima Batool and Lowri Griffiths and Edward Palmer and Andreas Karwath and Andrey Barsky and Georgios Gkoutos and Stephen Wood and Nicholas Barnes and Anthony S David and Gary Donohoe and Joanna Neill and Bill Deakin and Golam Khandaker and Rachel Upthegrove and PIMS collaboration},
doi = {10.1001/jamapsychiatry.2022.0407},
issn = {2168-622X},
year = {2022},
date = {2022-03-30},
urldate = {2022-03-30},
journal = {JAMA psychiatry},
volume = {2022},
pages = {1--11},
publisher = {Ämerican Medical Association},
abstract = {Importance: Previous in vitro and postmortem research suggests that inflammation may lead to structural brain changes via activation of microglia and/or astrocytic dysfunction in a range of neuropsychiatric disorders. Objective: To investigate the relationship between inflammation and changes in brain structures in vivo and to explore a transcriptome-driven functional basis with relevance to mental illness. Design, Setting, and Participants: This study used multistage linked analyses, including mendelian randomization (MR), gene expression correlation, and connectivity analyses. A total of 20688 participants in the UK Biobank, which includes clinical, genomic, and neuroimaging data, and 6 postmortem brains from neurotypical individuals in the Allen Human Brain Atlas (AHBA), including RNA microarray data. Data were extracted in February 2021 and analyzed between March and October 2021. Exposures: Genetic variants regulating levels and activity of circulating interleukin 1 (IL-1), IL-2, IL-6, C-reactive protein (CRP), and brain-derived neurotrophic factor (BDNF) were used as exposures in MR analyses. Main Outcomes and Measures: Brain imaging measures, including gray matter volume (GMV) and cortical thickness (CT), were used as outcomes. Associations were considered significant at a multiple testing-corrected threshold of P < 1.1 × 10-4. Differential gene expression in AHBA data was modeled in brain regions mapped to areas significant in MR analyses; genes were tested for biological and disease overrepresentation in annotation databases and for connectivity in protein-protein interaction networks. Results: Of 20688 participants in the UK Biobank sample, 10828 (52.3%) were female, and the mean (SD) age was 55.5 (7.5) years. In the UK Biobank sample, genetically predicted levels of IL-6 were associated with GMV in the middle temporal cortex (z score, 5.76; P = 8.39 × 10-9), inferior temporal (z score, 3.38; P = 7.20 × 10-5), fusiform (z score, 4.70; P = 2.60 × 10-7), and frontal (z score, -3.59; P = 3.30 × 10-5) cortex together with CT in the superior frontal region (z score, -5.11; P = 3.22 × 10-7). No significant associations were found for IL-1, IL-2, CRP, or BDNF after correction for multiple comparison. In the AHBA sample, 5 of 6 participants (83%) were male, and the mean (SD) age was 42.5 (13.4) years. Brain-wide coexpression analysis showed a highly interconnected network of genes preferentially expressed in the middle temporal gyrus (MTG), which further formed a highly connected protein-protein interaction network with IL-6 (enrichment test of expected vs observed network given the prevalence and degree of interactions in the STRING database: 43 nodes/30 edges observed vs 8 edges expected; mean node degree, 1.4; genome-wide significanc},
keywords = {artificial intelligence, health data science, medicine},
pubstate = {published},
tppubtype = {article}
}
2017
Karwath, Andreas; Hubrich, Markus; Kramer, Stefan
Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings, Springer Springer International Publishing, Cham, 2017, ISBN: 978-3-319-59758-4.
Abstract | Links | BibTeX | Tags: alzheimer, artificial intelligence, deep learning, health data science, machine learning, medicine, visualization
@conference{karwath2017a,
title = {Convolutional Neural Networks for the Identification of Regions of Interests in PET Scans: A Study of Representation Learning for Diagnosing Alzheimer's Disease},
author = {Andreas Karwath and Markus Hubrich and Stefan Kramer},
editor = {en Teije, Annette and Popow, Christian and Holmes, John H. and Sacchi, Lucia},
url = {http://dx.doi.org/10.1007/978-3-319-59758-4_36},
doi = {10.1007/978-3-319-59758-4_36},
isbn = {978-3-319-59758-4},
year = {2017},
date = {2017-06-21},
urldate = {2017-06-21},
booktitle = {Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings},
pages = {316-321},
publisher = {Springer International Publishing},
address = {Cham},
organization = {Springer},
abstract = {When diagnosing patients suffering from dementia based on imaging data like PET scans, the identification of suitable predictive regions of interest (ROIs) is of great importance. We present a case study of 3-D Convolutional Neural Networks (CNNs) for the detection of ROIs in this context, just using voxel data, without any knowledge given a priori. Our results on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that the predictive performance of the method is on par with that of state-of-the-art methods, with the additional benefit of potential insights into affected brain regions.},
keywords = {alzheimer, artificial intelligence, deep learning, health data science, machine learning, medicine, visualization},
pubstate = {published},
tppubtype = {conference}
}