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Pre-print on Associations between Morbidities in Small But Important Subgroup using a Bayesian approach

    Our paper on the “Associations between Morbidities in Small But Important Subgroups: A Novel Bayesian Approach for Robust Multimorbidity Analysis with Small Sample Sizes”  is out.

    Abstract:

    Background: Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when data is limited. We have developed a Bayesian inference framework that is robust to sparse data and have used it to quantify morbidity associations in the oldest old, a population with limited available data.

    Methods: We conducted a retrospective cross-sectional cohort study of a representative dataset of primary care patients in Scotland. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex, to study the effect of small sample sizes in the estimation of associations between long-term conditions. We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with a new measure of associations, Associations Beyond Chance (ABC), that utilises a Bayesian framework. To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC.

    Findings: Our Bayesian framework was appropriately more cautious in attributing association when evidence is small, as it dismissed six of the top ten associations reported by RR, most of which relate to uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex, for which ABC only reported as significant about one-fifth of those reported by RR. Last, the presence of potentially inaccurate associations by RR also affected the aggregated measures of multimorbidity and network representations.

    Interpretation: Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when data is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity.

    More information at SSRN: https://ssrn.com/abstract=4515875 or http://dx.doi.org/10.2139/ssrn.4515875.

    Paper on brain tumour survival predictions accepted in Computer Methods and Programs in Biomedicine

      Our paper on the “Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability” has been published in Computer Methods and Programs in Biomedicine. The work by Colleen Charlton, Michael Poon, Paul Brennan and Jacques Fleuriot looks at classification models for predicting brain tumour survival over one year, with an emphasis on interpretable/Explainable AI.  The paper can be accessed here.

      Survey pre-print on Neurosymbolic AI for Reasoning on Graph Structures is out on arXiv

        Our article on Neurosymbolic AI for Reasoning on Graph Structure is out. This is a comprehensive survey by Lauren Nicole DeLong, Ramon Fernández Mir, Matthew Whyte, Zonglin Ji and Jacques D. Fleuriot.

        Abstract:

        Neurosymbolic AI is an increasingly active area of research which aims to combine symbolic reasoning methods with deep learning to generate models with both high predictive performance and some degree of human-level comprehensibility. As knowledge graphs are becoming a popular way to represent heterogeneous and multi-relational data, methods for reasoning on graph structures have attempted to follow this neurosymbolic paradigm. Traditionally, such approaches have utilized either rule-based inference or generated representative numerical embeddings from which patterns could be extracted. However, several recent studies have attempted to bridge this dichotomy in ways that facilitate interpretability, maintain performance, and integrate expert knowledge. Within this article, we survey a breadth of methods that perform neurosymbolic reasoning tasks on graph structures. Within this article, we survey a breadth of methods that perform neurosymbolic reasoning tasks on graph structures. To better compare the various methods, we propose a novel taxonomy by which we can classify them. Specifically, we propose three major categories: (1) logically-informed embedding approaches, (2) embedding approaches with logical constraints, and (3) rule-learning approaches. Alongside the taxonomy, we provide a tabular overview of the approaches and links to their source code, if available, for more direct comparison. Finally, we discuss the applications on which these methods were primarily used and propose several prospective directions toward which this new field of research could evolve.

        The paper can be found on arXiv at arXiv:2302.07200 and an associated Github repository is here.

        New Project funded by the Edinburgh Laboratory for Integrated Artificial Intelligence (ELIAI)

          The Edinburgh Laboratory for Integrated Artificial Intelligence (ELIAI) will fund a new project on integrating theorem proving and neural learning led by Jacques Fleuriot in collaboration with Ram Ramamoorthy. The 2-year project will involve the development of theorem-proving framework that allows for formal representation and proofs, and then faithful code-generation, of user-specified, spatio-temporal constraints that can then be used for learning in scenarios involving human-robot interaction

          Opportunity: Multimorbidity PhD Programme for Health Professionals

            The Clinical PhD Research Fellowships are fixed term 3-year appointments offering training, mentoring and support to health professionals undertaking a PhD on the topic of multimorbidity.

            We are looking for applicants who are interested in working on understanding the “Co-existing mental and physical multimorbidity, adverse events, and longer term outcomes in hospitalised patients with sepsis“.

            More info available here.

            Paper accepted at CICM 2022

              Our paper, “Re-imagining the Isabelle Archive of Formal Proofs” (MacKenzie, Huch, Vaughan and Fleuriot), has been accepted at the 15th Conference on Intelligent Computer Mathematics (CICM 2022).

              New Archive of Formal Proof Website

                A project involving Carlin MacKenzie, James Vaughan and Jacques Fleuriot has resulted in a re-design and re-implementation of the Archive of Formal Proofs, which is a collection of proof libraries, examples, and larger scientific developments, mechanically checked in the theorem prover Isabelle. The revamped website (https://www.isa-afp.org) is now live and will serve the Isabelle community across the world. The Edinburgh team worked with Fabian Huch of TU Munich to integrate their work into the AFP infrastructure.