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2. Extending Defeasible Reasoning Beyond Rational Closure

Author

Slater, Luke, Meyer, Thomas, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pillay, Anban, editor, Jembere, Edgar, editor, and J. Gerber, Aurona, editor

Published
2023
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3. Ontology and text mining : methods and applications for hypertrophic cardiomyopathy and beyond

Author

Slater, Luke Thomas

Subjects
R Medicine (General)
Abstract

In this thesis we describe a number of contributions across the deeply interlinked domains of ontology, text mining, and prognostic modelling. We explore and evaluate ontology interoperability, and develop new methods for synonym expansion and negation detection in biomedical text. In addition to evaluating these pieces of work individually, we use them to form the basis of a text mining pipeline that can identify and phenotype patients across a clinical text record, which is used to reveal hundreds of University Hospitals Birmingham patients diagnosed with hypertrophic cardiomyopathy who are unknown to the specialist clinic. The work culminates in the text mining results being used to enable prognostic modelling of complication development in patients with hypertrophic cardiomyopathy, finding that routine blood markers, in addition to already well known variables, are powerful predictors.

Published
2020

6. A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

Author

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

Published
2022
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7. Aber-OWL: a framework for ontology-based data access in biology

Author

Hoehndorf, Robert, Slater, Luke, Schofield, Paul N., and Gkoutos, Georgios V.

Subjects
Computer Science - Databases, Computer Science - Information Retrieval, Quantitative Biology - Genomics
Abstract

Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of automated reasoning. We have developed the Aber-OWL infrastructure that provides reasoning services for bio-ontologies. Aber-OWL consists of an ontology repository, a set of web services and web interfaces that enable ontology-based semantic access to biological data and literature. Aber-OWL is freely available at http://aber-owl.net.

Published
2014
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8. Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study

Author

Carr, Ewan, Bendayan, Rebecca, Bean, Daniel, Stammers, Matt, Wang, Wenjuan, Zhang, Huayu, Searle, Thomas, Kraljevic, Zeljko, Shek, Anthony, Phan, Hang T. T., Muruet, Walter, Gupta, Rishi K., Shinton, Anthony J., Wyatt, Mike, Shi, Ting, Zhang, Xin, Pickles, Andrew, Stahl, Daniel, Zakeri, Rosita, Noursadeghi, Mahdad, O’Gallagher, Kevin, Rogers, Matt, Folarin, Amos, Karwath, Andreas, Wickstrøm, Kristin E., Köhn-Luque, Alvaro, Slater, Luke, Cardoso, Victor Roth, Bourdeaux, Christopher, Holten, Aleksander Rygh, Ball, Simon, McWilliams, Chris, Roguski, Lukasz, Borca, Florina, Batchelor, James, Amundsen, Erik Koldberg, Wu, Xiaodong, Gkoutos, Georgios V., Sun, Jiaxing, Pinto, Ashwin, Guthrie, Bruce, Breen, Cormac, Douiri, Abdel, Wu, Honghan, Curcin, Vasa, Teo, James T., Shah, Ajay M., and Dobson, Richard J. B.

Published
2021
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11. Harmonising knowledge for safer materials via the “NanoCommons” Knowledge Base

Author

Maier, Dieter, primary, Exner, Thomas E., additional, Papadiamantis, Anastasios G., additional, Ammar, Ammar, additional, Tsoumanis, Andreas, additional, Doganis, Philip, additional, Rouse, Ian, additional, Slater, Luke T., additional, Gkoutos, Georgios V., additional, Jeliazkova, Nina, additional, Ilgenfritz, Hilmar, additional, Ziegler, Martin, additional, Gerhard, Beatrix, additional, Kopetsky, Sebastian, additional, Joshi, Deven, additional, Walker, Lee, additional, Svendsen, Claus, additional, Sarimveis, Haralambos, additional, Lobaskin, Vladimir, additional, Himly, Martin, additional, van Rijn, Jeaphianne, additional, Winckers, Laurent, additional, Millán Acosta, Javier, additional, Willighagen, Egon, additional, Melagraki, Georgia, additional, Afantitis, Antreas, additional, and Lynch, Iseult, additional

Published
2023
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14. Experiences with Aber-OWL, an Ontology Repository with OWL EL Reasoning

Author

Slater, Luke, Rodríguez-García, Miguel Ángel, O’Shea, Keiron, Schofield, Paul N., Gkoutos, Georgios V., Hoehndorf, Robert, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tamma, Valentina, editor, Dragoni, Mauro, editor, Gonçalves, Rafael, editor, and Ławrynowicz, Agnieszka, editor

Published
2016
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16. Harmonising knowledge for safer materials via the “NanoCommons” Knowledge Base

Author

Maier, Dieter, Exner, Thomas E., Papadiamantis, Anastasios G., Ammar, Ammar, Tsoumanis, Andreas, Doganis, Philip, Rouse, Ian, Slater, Luke T., Gkoutos, Georgios V., Jeliazkova, Nina, Ilgenfritz, Hilmar, Ziegler, Martin, Gerhard, Beatrix, Kopetsky, Sebastian, Joshi, Deven, Walker, Lee, Svendsen, Claus, Sarimveis, Haralambos, Lobaskin, Vladimir, Himly, Martin, van Rijn, Jeaphianne, Winckers, Laurent, Millán Acosta, Javier, Willighagen, Egon, Melagraki, Georgia, Afantitis, Antreas, Lynch, Iseult, Maier, Dieter, Exner, Thomas E., Papadiamantis, Anastasios G., Ammar, Ammar, Tsoumanis, Andreas, Doganis, Philip, Rouse, Ian, Slater, Luke T., Gkoutos, Georgios V., Jeliazkova, Nina, Ilgenfritz, Hilmar, Ziegler, Martin, Gerhard, Beatrix, Kopetsky, Sebastian, Joshi, Deven, Walker, Lee, Svendsen, Claus, Sarimveis, Haralambos, Lobaskin, Vladimir, Himly, Martin, van Rijn, Jeaphianne, Winckers, Laurent, Millán Acosta, Javier, Willighagen, Egon, Melagraki, Georgia, Afantitis, Antreas, and Lynch, Iseult

Abstract

In mediaeval Europe, the term “commons” described the way that communities managed land that was held “in common” and provided a clear set of rules for how this “common land” was used and developed by, and for, the community. Similarly, as we move towards an increasingly knowledge-based society where data is the new oil, new approaches to sharing and jointly owning publicly funded research data are needed to maximise its added value. Such common management approaches will extend the data’s useful life and facilitate its reuse for a range of additional purposes, from modelling, to meta-analysis to regulatory risk assessment as examples relevant to nanosafety data. This “commons” approach to nanosafety data and nanoinformatics infrastructure provision, co-development, and maintenance is at the heart of the “NanoCommons” project and underpins its post-funding transition to providing a basis on which other initiatives and projects can build. The present paper summarises part of the NanoCommons infrastructure called the NanoCommons Knowledge Base. It provides interoperability for nanosafety data sources and tools, on both semantic and technical levels. The NanoCommons Knowledge Base connects knowledge and provides both programmatic (via an Application Programming Interface) and a user-friendly graphical interface to enable (and democratise) access to state of the art tools for nanomaterials safety prediction, NMs design for safety and sustainability, and NMs risk assessment, as well. In addition, the standards and interfaces for interoperability, e.g., file templates to contribute data to the NanoCommons, are described, and a snapshot of the range and breadth of nanoinformatics tools and models that have already been integrated are presented Finally, we demonstrate how the NanoCommons Knowledge Base can support users in the FAIRification of their experimental workflows and how the NanoCommons Knowledge Base itself has progressed towards richer compliance with the FAIR pr

Published
2023

17. Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium

Author

Wilson, Lydia J., primary, Kiffer, Frederico C., additional, Berrios, Daniel C., additional, Bryce-Atkinson, Abigail, additional, Costes, Sylvain V., additional, Gevaert, Olivier, additional, Matarèse, Bruno F. E., additional, Miller, Jack, additional, Mukherjee, Pritam, additional, Peach, Kristen, additional, Schofield, Paul N., additional, Slater, Luke T., additional, and Langen, Britta, additional

Published
2023
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18. Klarigi: Characteristic explanations for semantic biomedical data

Author

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

Published
2023
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20. Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.

Author

Gill, Simrat K, Karwath, Andreas, Uh, Hae-Won, Cardoso, Victor Roth, Gu, Zhujie, Barsky, Andrey, Slater, Luke, Acharjee, Animesh, Duan, Jinming, Dall'Olio, Lorenzo, Bouhaddani, Said el, Chernbumroong, Saisakul, Stanbury, Mary, Haynes, Sandra, Asselbergs, Folkert W, Grobbee, Diederick E, Eijkemans, Marinus J C, Gkoutos, Georgios V, Kotecha, Dipak, and group, BigData@Heart Consortium and the cardAIc

Subjects
ARTIFICIAL intelligence, MEDICAL personnel, MACHINE learning, DISEASE management, MEDICAL care
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. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis

Author

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

Published
2021
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25. Development and application of the ocular immune-mediated inflammatory diseases ontology enhanced with synonyms from online patient support forum conversation

Author

Pendleton, Samantha C., primary, Slater, Luke T., additional, Karwath, Andreas, additional, Gilbert, Rose M., additional, Davis, Nicola, additional, Pesudovs, Konrad, additional, Liu, Xiaoxuan, additional, Denniston, Alastair K., additional, Gkoutos, Georgios V., additional, and Braithwaite, Tasanee, additional

Published
2021
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26. Klarigi: Characteristic Explanations for Semantic Data

Author

Slater, Luke T, primary, Williams, John A, additional, Schofield, Paul N, additional, Russell, Sophie, additional, Pendleton, Samantha C, additional, Karwath, Andreas, additional, Fanning, Hilary, additional, Ball, Simon, additional, Hoehndorf, Robert T, additional, and Gkoutos, Georgios V, additional

Published
2021
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30. Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

Author

Lynch, Iseult, Anreas Afantitis, Exner, Thomas, Himley, Martin, Lobaskin, Vladimir, Doganis, Phillip, Maier, Dieter, Sanabria, Natasha, Rybinska-Fryca, Anna, Gromelski, Maciej, Puzyn, Tomasz, Willighagen, Egon, Johnston, Blair, Gulumian, Mary, Matzke, Marianne, Etxabe, Amaia Green, Bossa, Nathan, Serra, Angela, Liampa, Irene, Harper, Stacey, Tämm, Kaido, Jensen, Alexander CØ, Kohonen, Pekka, Slater, Luke, Tsoumanis, Andreas, Greco, Dario, Winkler, David, Haralambos Sarimveis, and Melagraki, Georgia

Subjects
FOS: Nanotechnology, 100708 Nanomaterials
Abstract

Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analysed issues involved in developing an InChI for NMs (NInChI). The layers needed to captureNMstructures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a “nano” extension to the InChI standard.

Published
2020
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32. Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

Author

Lynch, Iseult, primary, Afantitis, Antreas, additional, Exner, Thomas, additional, Himly, Martin, additional, Lobaskin, Vladimir, additional, Doganis, Philip, additional, Maier, Dieter, additional, Sanabria, Natasha, additional, Papadiamantis, Anastasios G., additional, Rybinska-Fryca, Anna, additional, Gromelski, Maciej, additional, Puzyn, Tomasz, additional, Willighagen, Egon, additional, Johnston, Blair D., additional, Gulumian, Mary, additional, Matzke, Marianne, additional, Green Etxabe, Amaia, additional, Bossa, Nathan, additional, Serra, Angela, additional, Liampa, Irene, additional, Harper, Stacey, additional, Tämm, Kaido, additional, Jensen, Alexander CØ, additional, Kohonen, Pekka, additional, Slater, Luke, additional, Tsoumanis, Andreas, additional, Greco, Dario, additional, Winkler, David A., additional, Sarimveis, Haralambos, additional, and Melagraki, Georgia, additional

Published
2020
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37. Aging Neuro-Behavior Ontology

Author

Martínez-Santiago, Fernando, primary, García-Viedma, M. Rosario, additional, Williams, John A., additional, Slater, Luke T., additional, and Gkoutos, Georgios V., additional

Published
2020
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44. The Development and Piloting of an Early Youth‐Engagement (EYE) Model to Improve Engagement of Young People in First Episode Psychosis Services: A Mixed Methods Study.

Author

Greenwood, Kathryn, Chandler, Ruth, Labuschagne, Kirsty, Peters, Emmanuelle, Alford, Katie, Visser, Richard, Field, Andy, Slater, Luke, and Garety, Philippa

Subjects
*YOUNG adults, *SOCIAL networks, *YOUTH services, *PSYCHOSES, *DESIGN services
Abstract

ABSTRACT Aim Method Results Conclusion Psychosis is associated with significant health and societal costs. Early intervention in psychosis services (EIP) are highly effective in promoting recovery, yet substantial proportions of young people disengage. The current study aimed to develop and evaluate a novel engagement intervention in EIP services.A qualitative investigation of facilitators and barriers to engagement in 68 first episode psychosis patients, family members and young people, and a Delphi consultation with 27 regional and national youth and psychosis service leads informed the development of the intervention.A mixed‐methods feasibility‐pilot study then compared engagement outcomes in 298 EIP service users in two cohorts: standard EIP versus standard EIP plus the novel early youth‐engagement (EYE) intervention. A qualitative study explored intervention experiences in 22 randomly selected service users, carers and clinicians. A process evaluation explored delivery.Disengagement was 24% in the standard EIP cohort compared to 14.5% in the standard EIP plus EYE intervention cohort. A 95% Bayesian credibility interval revealed a 95% probability that the true reduction in disengagement lay somewhere between 0% and 18%. The number needed to treat was 11, 95% CI [5, 242]. Use of the EYE resources was associated with engagement. Qualitiative feedback supported effects on communication, social network engagement, service user goals, mental health and well‐being outcomes.The EYE intervention was designed from a service user, young person and carer perspective. Both qualitative and quantitative data support impacts on engagement. We now need to evaluate effectiveness, cost‐effectiveness and implementation in a multi‐site randomised controlled trial. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare

Author

Gill, Simrat K, Karwath, Andreas, Uh, Hae-Won, Cardoso, Victor Roth, Gu, Zhujie, Barsky, Andrey, Slater, Luke, Acharjee, Animesh, Duan, Jinming, Dall'Olio, Lorenzo, el Bouhaddani, Said, Chernbumroong, Saisakul, Stanbury, Mary, Haynes, Sandra, Asselbergs, Folkert W, Grobbee, Diederick E, Eijkemans, Marinus J C, Gkoutos, Georgios V, Kotecha, Dipak, Bunting, Karina V, Tica, Otilia, Mobley, Alastair R, Wang, Xiaoxia, Champsi, Asgher, Haider, Nafeesah Ahmad, Ventura, Maximina, Young, Alice, McGreavy, Paul, Castellani, Gastone, Bradlow, William, O'Regan, Declan, and Center, Julius

Subjects
Cardiology and Cardiovascular Medicine
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.

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46. Box of delights.

Author

Slater, Luke

Subjects
SOUND recordings
Abstract

Presents Luke Slater's preferred sound recordings.

Published
1994

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