We are proud to announce that AI Modelling Lab PhD Student Fiona Smith’s exhibition “The BOX” is running from the 6th to the 19th of April 2024 as part of the Edinburgh Science Festival. Fiona is funded by our Centre for Doctoral Traning in Biomedical AI and was selected to be the Creator in Residence at Fraunhofer MEVIS.
The live exhibition can be visited at Inspace, Crichton St, University of Edinburgh.
Congratulations to Jorge Gatete Villegas who succesfully defended his thesis on Modelling and Predicting Medical Outcomes for Intensive Care Patients via Network Mechanisms and Machine
Learning. His examiners were Dr Areti Manataki of the University of St Andrews and Dr Sohan Seth of the University of Edinburgh.
Changing care for people with short life expectancy is recommended, but performance of existing tools that predict 1-3 year mortality isn’t usually very good. We need to do better! See our new systematic review at: https://doi.org/10.1016/S2666-7568(23)00264-7.
Applications are invited for a Research Associate in Care Technologies in Context. The employment will be until the 31st of December 2025.
This post will contribute to the Integrated Technologies of Care and the Understanding the Person in Context Programmes within the Advanced Care Research Centre (ACRC) at the University of Edinburgh.
More information is available here.
Fully Funded PhD in Predicting harm from prescribed drugs in people with polypharmacy, multimorbidity and frailty.
The aim of this project is to develop and validate new models to predict who is at risk of being harmed by prescribed drugs, focusing on the outcome of acute kidney injury. More information available here.
Paola Galdi and Jacques Fleuriot are editing a special issue of the journal Mathematics in Computer Science focused on the interplay between mathematics and AI. The Call for Papers can be found here. The submission deadline is on the 31st of January 2024.
The paper on “Alignment-based conformance checking over probabilistic events” by Jiawei Zheng, Petros Papapanagiotou and Jacques Fleuriot has been accepted at the Hawaii International Conference on System Sciences (HICSS-57).
Abstract:
Conformance checking techniques allow us to evaluate how well some exhibited behaviour, represented by a trace of monitored events, conforms to a specified process model. Modern monitoring and activity recognition technologies, such as those relying on sensors, the IoT, statistics and AI, can produce a wealth of relevant event data. However, this data is typically characterised by noise and uncertainty, in contrast to the assumption of a deterministic event log required by conformance checking algorithms. In this paper, we extend alignment-based conformance checking to function under a probabilistic event log.
We introduce a weighted trace model and weighted alignment cost function, and a custom threshold parameter that controls the level of confidence on the event data vs. the process model. The resulting algorithm considers activities of lower but sufficiently high probability that better align with the process model. We explain the algorithm and its motivation both from formal and intuitive perspectives, and demonstrate its functionality in comparison with deterministic alignment using real-life datasets.
Keywords: Conformance checking, Probabilistic events, Uncertainty, Probabilistic cost function
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.
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.
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.