publications

@article{Slater_2023,

title = {Klarigi: Characteristic explanations for semantic biomedical data},

author = {Luke T. Slater and John A. Williams and Paul N. Schofield and Sophie Russell and Samantha C. Pendleton and Andreas Karwath and Hilary Fanning and Simon Ball and Robert Hoehndorf and Georgios V. Gkoutos},

url = {https://doi.org/10.1016%2Fj.compbiomed.2022.106425},

doi = {10.1016/j.compbiomed.2022.106425},

year  = {2023},

date = {2023-02-01},

urldate = {2023-02-01},

journal = {Computers in Biology and Medicine},

volume = {153},

pages = {106425},

publisher = {Elsevier BV},

keywords = {artificial intelligence, health data science, NLP},

pubstate = {published},

tppubtype = {article}

}

@article{9b9767f517a040f4822591145f8c61a8,

title = {Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare: artificial intelligence framework},

author = {Simrat Gill and Andreas Karwath and Hae-Won Uh and Victor Roth Cardoso and Zhujie Gu and Andrey Barsky and Luke Slater and Animesh Acharjee and Jinming Duan and Lorenzo DallÓlio and Said el Bouhaddani and Saisakul Chernbumroong and Mary Stanbury and Sandra Haynes and Folkert W Asselbergs and Diederick Grobbee and Marinus Eijkemans and Georgios Gkoutos and Dipak Kotecha and group BigData@Heart Consortium },

doi = {10.1093/eurheartj/ehac758},

issn = {0195-668X},

year  = {2023},

date = {2023-01-11},

urldate = {2023-01-11},

journal = {European Heart Journal},

publisher = {Öxford University Press},

abstract = {Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management.Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity."},

note = {textcopyright The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.},

keywords = {artificial intelligence, cardiology, EHR, health data science},

pubstate = {published},

tppubtype = {article}

}

@article{Wu_2022,

title = {A survey on clinical natural language processing in the United Kingdom from 2007 to 2022},

author = {Honghan Wu and Minhong Wang and Jinge Wu and Farah Francis and Yun-Hsuan Chang and Alex Shavick and Hang Dong and Michael T. C. Poon and Natalie Fitzpatrick and Adam P. Levine and Luke T. Slater and Alex Handy and Andreas Karwath and Georgios V. Gkoutos and Claude Chelala and Anoop Dinesh Shah and Robert Stewart and Nigel Collier and Beatrice Alex and William Whiteley and Cathie Sudlow and Angus Roberts and Richard J. B. Dobson},

url = {https://doi.org/10.1038%2Fs41746-022-00730-6},

doi = {10.1038/s41746-022-00730-6},

year  = {2022},

date = {2022-12-01},

urldate = {2022-12-01},

journal = {npj Digital Medicine},

volume = {5},

number = {1},

publisher = {Springer Science and Business Media LLC},

keywords = {artificial intelligence, health data science, NLP},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{Karwath_2021,

title = {Redefining beta-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis},

author = {Andreas Karwath and Karina V Bunting and Simrat K Gill and Otilia Tica and Samantha Pendleton and Furqan Aziz and Andrey D Barsky and Saisakul Chernbumroong and Jinming Duan and Alastair R Mobley and Victor Roth Cardoso and Luke Slater and John A Williams and Emma-Jane Bruce and Xiaoxia Wang and Marcus D Flather and Andrew J S Coats and Georgios V Gkoutos and Dipak Kotecha},

url = {https://doi.org/10.1016%2Fs0140-6736%2821%2901638-x},

doi = {10.1016/s0140-6736(21)01638-x},

year  = {2021},

date = {2021-08-01},

urldate = {2021-08-01},

journal = {The Lancet},

publisher = {Elsevier BV},

abstract = {Background

Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of β-blocker efficacy in patients with sinus rhythm and atrial fibrillation.

Methods

Neural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of β blockers. All-cause mortality during median 1·3 years of follow-up was assessed by intention to treat, stratified by electrocardiographic heart rhythm. The number of clusters and dimensions was determined objectively, with results validated using a leave-one-trial-out approach. This study was prospectively registered with ClinicalTrials.gov (NCT00832442) and the PROSPERO database of systematic reviews (CRD42014010012).

Findings

15 659 patients with heart failure and LVEF of less than 50% were included, with median age 65 years (IQR 56–72) and LVEF 27% (IQR 21–33). 3708 (24%) patients were women. In sinus rhythm (n=12 822), most clusters demonstrated a consistent overall mortality benefit from β blockers, with odds ratios (ORs) ranging from 0·54 to 0·74. One cluster in sinus rhythm of older patients with less severe symptoms showed no significant efficacy (OR 0·86, 95% CI 0·67–1·10; p=0·22). In atrial fibrillation (n=2837), four of five clusters were consistent with the overall neutral effect of β blockers versus placebo (OR 0·92, 0·77–1·10; p=0·37). One cluster of younger atrial fibrillation patients at lower mortality risk but similar LVEF to average had a statistically significant reduction in mortality with β blockers (OR 0·57, 0·35–0·93; p=0·023). The robustness and consistency of clustering was confirmed for all models (p<0·0001 vs random), and cluster membership was externally validated across the nine independent trials.

Interpretation

An artificial intelligence-based clustering approach was able to distinguish prognostic response from β blockers in patients with heart failure and reduced LVEF. This included patients in sinus rhythm with suboptimal efficacy, as well as a cluster of patients with atrial fibrillation where β blockers did reduce mortality.

Funding

Medical Research Council, UK, and EU/EFPIA Innovative Medicines Initiative BigData@Heart.},

keywords = {artificial intelligence, clustering, crossvalidation, deep learning, EHR, health data science, phenotypes, validation},

pubstate = {published},

tppubtype = {article}

}

@article{Slater_2021,

title = {Towards similarity-based differential diagnostics for common diseases},

author = {Luke T Slater and Andreas Karwath and John A Williams and Sophie Russell and Silver Makepeace and Alexander Carberry and Robert Hoehndorf and Georgios V Gkoutos},

url = {https://doi.org/10.1016%2Fj.compbiomed.2021.104360},

doi = {10.1016/j.compbiomed.2021.104360},

year  = {2021},

date = {2021-06-01},

urldate = {2021-06-01},

journal = {Computers in Biology and Medicine},

volume = {133},

pages = {104360},

publisher = {Elsevier BV},

keywords = {artificial intelligence, health data science, NLP, semantic similarity},

pubstate = {published},

tppubtype = {article}

}

@article{RN19,

title = {Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study},

author = {E Carr and R Bendayan and D Bean and M Stammers and W Wang and H Zhang and T Searle and Z Kraljevic and A Shek and H T T Phan and W Muruet and R K Gupta and A J Shinton and M Wyatt and T Shi and X Zhang and A Pickles and D Stahl and R Zakeri and M Noursadeghi and K O'Gallagher and M Rogers and A Folarin and Andreas Karwath and K E Wickstrøm and A Köhn-Luque and L Slater and V R Cardoso and C Bourdeaux and A R Holten and S Ball and C McWilliams and L Roguski and F Borca and J Batchelor and E K Amundsen and X Wu and G V Gkoutos and J Sun and A Pinto and B Guthrie and C Breen and A Douiri and H Wu and V Curcin and J T Teo and A M Shah and R J B Dobson},

doi = {10.1186/s12916-020-01893-3},

issn = {1741-7015},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {BMC Med},

volume = {19},

number = {1},

pages = {23},

keywords = {artificial intelligence, COVID-19, early warning score, health data science, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{PMID:33692093,

title = {Improving the diagnosis of heart failure in patients with atrial fibrillation.},

author = {Bunting KV and Gill SK and Sitch A and Mehta S and O'Connor K and Lip GY and Kirchhof P and Strauss VY and Rahimi K and Camm AJ and Stanbury M and Griffith M and Townend JN and Gkoutos GV and RAte Therapy Evaluation Atrial Fibrillation (RATE-AF) control in permanent trial group},

doi = {10.1136/heartjnl-2020-318557},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Heart (British Cardiac Society)},

keywords = {artificial intelligence, cardiology, health data science},

pubstate = {published},

tppubtype = {article}

}

@article{RN18,

title = {Ensemble learning for poor prognosis predictions: a case study on SARS-CoV2},

author = {H Wu and H Zhang and Andreas Karwath and Z Ibrahim and T Shi and X Zhang and K Wang and J Sun and K Dhaliwal and D Bean and V R Cardoso and K Li and J T Teo and A Banerjee and F Gao-Smith and T Whitehouse and T Veenith and G V Gkoutos and X Wu and R Dobson and B Guthrie},

doi = {10.1093/jamia/ocaa295},

issn = {1067-5027 (Print) 1067-5027},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {J Am Med Inform Assoc},

keywords = {artificial intelligence, COVID-19, health data science, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{koeppel2019,

title = {Pairwise Learning to Rank by Neural Networks  Revisited: Reconstruction, Theoretical Analysis and  Practical Performance},

author = {Marius Köppel and Alexander Segner and Martin Wagener and Lukas Pensel and Andreas Karwath and Stefan Kramer},

url = {http://arxiv.org/abs/1909.02768},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {CoRR},

keywords = {artificial intelligence, learning to rank},

pubstate = {published},

tppubtype = {article}

}

@article{geilke2018a,

title = {Online estimation of discrete, continuous, and conditional joint densities using classifier chains},

author = {Michael Geilke and Andreas Karwath and Eibe Frank and Stefan Kramer},

url = {https://doi.org/10.1007/s10618-017-0546-6},

doi = {10.1007/s10618-017-0546-6},

issn = {1384-5810},

year  = {2018},

date = {2018-05-01},

urldate = {2018-05-01},

journal = {Data Mining and Knowledge Discovery},

volume = {32},

number = {3},

pages = {561-603},

publisher = {Springer US},

abstract = {We address the problem of estimating discrete, continuous, and conditional joint densities online, i.e., the algorithm is only provided the current example and its current estimate for its update. The family of proposed online density estimators, estimation of densities online (EDO), uses classifier chains to model dependencies among features, where each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains and ensembles of weighted classifier chains. For all density estimators, we provide consistency proofs and propose algorithms to perform certain inference tasks. The empirical evaluation of the estimators is conducted in several experiments and on datasets of up to several millions of instances. In the discrete case, we compare our estimators to density estimates computed by Bayesian structure learners. In the continuous case, we compare them to a state-of-the-art online density estimator. Our experiments demonstrate that, even though designed to work online, EDO delivers estimators of competitive accuracy compared to other density estimators (batch Bayesian structure learners on discrete datasets and the state-of-the-art online density estimator on continuous datasets). Besides achieving similar performance in these cases, EDO is also able to estimate densities with mixed types of variables, i.e., discrete and continuous random variables.},

keywords = {artificial intelligence, data mining, density estimation, machine learning, stream mining},

pubstate = {published},

tppubtype = {article}

}

@article{grzonka2012,

title = {Activity-Based Estimation of Human Trajectories},

author = {Slawomir Grzonka and Andreas Karwath and Frederic Dijoux and Wolfram Burgard},

url = {http://dx.doi.org/10.1109/TRO.2011.2165372},

doi = {10.1109/TRO.2011.2165372},

year  = {2012},

date = {2012-02-02},

urldate = {2012-02-02},

journal = {IEEE Transactions on Robotics},

volume = {28},

number = {1},

pages = {234-245},

abstract = {We present a novel approach to incrementally determine the trajectory of a person in 3-D based on its motions and activities in real time. In our algorithm, we estimate the motions and activities of the user given the data that are obtained from a motion capture suit equipped with several inertial measurement units. These activities include walking up and down staircases, as well as opening and closing doors. We interpret the first two types of activities as motion constraints and door-handling events as landmark detections in a graph-based simultaneous localization and mapping (SLAM) framework. Since we cannot distinguish between individual doors, we employ a multihypothesis tracking approach on top of the SLAM procedure to deal with the high data-association uncertainty. As a result, we are able to accurately and robustly recover the trajectory of the person. Additionally, we present an algorithm to build approximate geometrical and topological maps based on the estimated trajectory and detected activities. We evaluate our approach in practical experiments that are carried out with different subjects and in various environments.},

keywords = {activity recognition, artificial intelligence, machine learning, simultaneous localization and mapping},

pubstate = {published},

tppubtype = {article}

}