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Joint AIML/FHAIVE Workshop Talks

    Date: 4th October 2023
    Time: 09:00 - 11:00

    We will be running a joint workshop with members of FHAIVE, Dario Greco's research group based in Tampere, Finland. We will explore some of the applications of AI in the biomedical domain.

    Local Organiser: Paola Galdi

    The Programme is as follows:

    Opening: 9:00 –9:05

    • 9:05 – 9:20: Building knowledge graphs from electronic health records for adverse event prediction (Paola, AIML)
      Abstract: With older age, there is an increased chance of being diagnosed with more than one long-term condition. The medical treatment of patients with multiple conditions is challenging because the interactions of symptoms and medications are complex and hard to predict. In this talk, I will discuss an ongoing project using knowledge-graph methods to detect people who are likely to have unexpected health problems (like falls or bleeding), with the ultimate goal of supporting doctors in the choice of proper treatment and preventive care. I will briefly introduce the Clinical Practice Research Datalink (CPRD) dataset and the data model underlying the knowledge graph. I will then present a first attempt at repurposing a knowledge graph recommender system (KGAT) in the context of adverse events predictions. I will conclude with an overview of the challenges and open questions left to address.  
    • 9:20 – 9:35: Graph learning for toxicology and chemical safety assessment- (Angela, FHAIVE)
      Integrating diverse data sources has become crucial to accurately predict the characteristics of drugs and chemicals and uncover novel associations between chemical exposure and human diseases. We developed a knowledge graph that contains manually curated relevant toxicological information related to drugs and chemicals. We started investigating the effectiveness of different network embedding algorithms and the predictive power of their features. We exploited the content of our knowledge graph in multiple applications, including the retrieval of relevant genes involved in COVID-19 pathogenesis and their targeting therapeutics, prediction of the potential side effects of drugs and comparison of tissues and cell line transcriptomic alterations.  
    • 9:35 – 9:50: Systems pharmacology for drug discovery (Tonino, FHAIVE)
      In recent years, the explosion in the amount of designed chemicals determined a “big bang” of the chemical universe. Such a wealth of chemical structures significantly boosted the possibilities to identify molecules with pharmacological properties for the treatment of a plethora of human complex diseases. Moreover, we hypothesize that modelling the complexity of the molecular buildup of diseases can be a concrete means to identify effective drug candidates. To this aim, network models are at the forefront to face this challenge, since they allow to investigate the molecular interactions sustaining physiological and pathological processes. By investigating aberrant patterns of connectivity in disease network models it is possible to pinpoint known and unknown molecular determinants of complex phenotypes, driving drug discovery predictions towards concrete pharmacological solutions. 

    Break: 9:50 – 10:00

    • 10:00 – 10:15: A multi-dimensional disease map (Lena, FHAIVE)
      To overcome phenotype-based disease definitions and gain a mechanistic disease understanding, a multi-dimensional view is needed. Combining multiple data layers generates a more informed picture of disease similarity than looking at single dimensions. We mapped the relationships of 500 diseases based on a consensus of six data dimensions including genomic, clinical, and pharmacological data. 
    • 10:15 – 10:30: A Bayesian network approach for a robust estimate of disease co-occurrence (Guillermo, AIML)
      Abstract: Examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity, but is challenging when data is limited. Previous literature in multimorbidity typically relies on association measures that are flawed when using small sample sizes, do not report confidence intervals or otherwise appropriately account for uncertainty, which is crucial in measures such as Relative Risk, as they are known to overestimate rare events. We have developed a Bayesian inference framework that is robust to small data samples and used it to quantify morbidity associations in the oldest old, a population with limited available data. We analysed associations obtained with Relative Risk (RR), and compared them with our proposed measure, Associations Beyond Chance (ABC), examining both parirwise associations and network aggregations. Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in less common conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations. 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. 
    • 10:30 – 10:45: MARS: A Neurosymbolic System for Biomedical Mechanism-of-Action Retrieval (Lauren, AIML)
      Abstract: Recently, several machine learning approaches have aided drug discovery by identifying promising candidates and predicting potential indications. However, understanding the ways in which drugs achieve their therapeutic effects, otherwise known as their mechanisms-of-action (MoA), is important for understanding potency, side effects, and interactions with various tissue types, among other things. We leveraged and improved the interpretability of a neurosymbolic reinforcement learning method in an attempt to reveal MoAs. While doing so, we observed that our findings raised several concerns with the reasoning process. Specifically, we debate situations in which patterns following a "guilt-by-association" trend are useful for predictions regarding novel compounds. We present our results to facilitate discussion about how generalizable ML-based models are to the drug discovery process as well as how important interpretability can be to such models.

    Discussion/Closing: 10:45 – 11:00


    Exploring AI Based Approaches for Post-operative Microvascular Free Flap Monitoring

      Date: 2nd October 2023
      Time: 09:00 - 11:00

      Title:  Exploring AI Based Approaches for Post-operative Microvascular Free Flap Monitoring
      Speakers: Fiona Smith

      Microvascular free flap surgery is a key technique utilised for the reconstruction of complex tissue deficits secondary to trauma and cancer. Whilst overall success rates are very good, this is only achieved because of close postoperative monitoring that allows the prompt identification of complications that require immediate re-operation for salvage. The current gold standard for free flap monitoring is regular clinical assessment of the patient at the beside but this is subjective, very labour intensive and disruptive for patients’ sleep. This PhD project aims to explore AI-based approaches for the postoperative monitoring of microvascular free flaps that are as effective and accurate as clinical assessment at identifying compromised free flaps but which are less subjective.  In this talk I will discuss the progress that has been made in the first year towards this aim. Firstly, I will outline the project protocols that have been designed for the acquisition of a free flap database with approvals for AI work. Secondly, I will describe the scoping review of current practices for ethical AI analysis of medical image datasets that was prompted by this. Finally, the results of early experiments to use a convolutional neural network to segment images of free flaps will be discussed and the plans for future work will be given.



      The Usage and Improvement of Neurosymbolic AI for Biomedical Applications

        Date: 27th September 2023
        Time: 09:15 - 10:30

        Title:  The Usage and Improvement of Neurosymbolic AI for Biomedical Applications
        Speakers: Lauren DeLong

        Neurosymbolic AI describes a hybrid field of AI between symbolic methods, which tend to be robust and interpretable, and deep learning methods, which are more scalable and tend to perform more competitively. Since these two areas tend to see tradeoffs between the types of benefits they offer, methods in neurosymbolic AI try to leverage the perks of each while mitigating their respective weaknesses.  As neurosymbolic AI has recently increased in popularity, many various approaches have been introduced, and several of these approaches possess unique features that neither symbolic nor deep learning methods had alone. Consequently, neurosymbolic methods have potential to provide novel solutions to longstanding research challenges. The goal of my thesis is to use and improve neurosymbolic AI to demonstrate how these unique characteristics are especially useful for challenges which are particularly prominent in biomedical data science. Specifically, I will discuss challenges like meaningful multimodal data  integration for clinical datasets, discovering drug mechanisms of action via long-range dependencies, and few shot learning to predict and understand rare, serious side effects.



        Predicting in-hospital mortality for ICU patients with liver disease using process mining and deep learning

          Date: 17th August 2023
          Time: 14:00 - 15:30

          Title:  Predicting in-hospital mortality for ICU patients with liver disease using process mining and deep learning
          Speakers: Zonglin Ji

          Intensive Care Unit (ICU) patients with liver disease present a unique clinical challenge due to increased risks of complications and mortality. Traditional severity scoring methods, while helpful, do not comprehensively consider a patient's prior medical history nor the temporal events associated with their stay, and are thus limited when it comes to providing an up-to-date understanding of a patient’s condition as it evolves. This research aims to incorporate time series events from the care pathways of patients to improve on this situation and enable better survival predictions over time. In this talk, we describe our work on combining Process Mining and Deep Learning and applying it to mortality predictions for ICU patients with liver diseases.



          Process-aware pattern recognition and anomaly detection under uncertainty

            Date: 19th July 2023
            Time: 12:30 - 14:00

            Title:  Process-aware pattern recognition and anomaly detection under uncertainty
            Speakers: Jiawei Zheng

            In today's increasingly digital world, recognising patterns and detecting anomalies have become increasingly crucial for ensuring efficient operations, enhancing productivity and promoting human healthy. Processes often embody contextual information, such as domain-specific knowledge, optimal or expected behaviour, or established best practices. By considering the process information from context, we can interpret patterns more accurately and enhance the performance of anomaly detection. However, the structure of underlying processes in different domains varies greatly. Due to this, the patterns and anomalies we are interested in have different focuses and come from different perspectives, and thus pose different challenges in different domains.

            In this talk, I will present the problems associated with incorporating process knowledge for pattern recognition and anomaly detection over uncertain data in heterogenous domains, the current progress that I have achieved, and finally the future plans to finish the project.

            Probabilistic Resources

              Date: 30th June 2023
              Time: 15:00 - 16:00

              Title: Probabilistic Resources
              Speakers: Filip Smola

              When composing processes, non-deterministic resources allow us to express uncertainty such as decisions, sensing or possible failure. However, in practice we want to know not only the possible cases but also their relative probabilities. We may use these to compute the expected value of some performance metric or to uncover impossible execution paths.

              In this talk I will present our work on adding these probabilities to a theory of process composition mechanised in Isabelle/HOL. I will give a high-level overview of how the probabilistic information is represented, the issues we encounter and how we address those.

              Algebraic Formalisation with locales, types and relations

                Date: 27th June 2023
                Time: 10:00 - 11:00

                Title:  Algebraic Formalisation with locales, types and relations
                Speakers: Richard Schmoetten

                Different approaches have been explored when formalising mathematical structures in Isabelle/HOL, roughly split between the set-based approach using locales and type-based classes. We present our formal theories in Lie groups and real algebra to introduce both ideas, and examine a relation-based way of reasoning about algebraic morphisms in a type class. This has the advantage of integrating directly with automated tooling in Isabelle (the transfer package), and may present an alternative to the current trend of recasting type-based theories as set-based ones (e.g. the Types-to-Sets package).

                Standards for Reporting Artificial Intelligence Research using Burns Image Datasets

                  Date: 2nd June 2023
                  Time: 14:00 - 16:00

                  Title:  Standards for Reporting Artificial Intelligence Research using Burns Image Datasets
                  Speakers: Fiona Smith

                  In recent years there has been increasing interest in AI applications in Plastic Surgery. In particular, the additional objectivity that computer vision approaches could bring to otherwise subjective visual analysis has been identified as being of particular utility to a speciality which concerns itself with the restoration of form and function. The literature includes several systematic reviews of proof-of-concept studies and early applications of computer vision approaches in the Plastic Surgery domain but their conclusions have often been hindered by a lack of standardised reporting of methods and results.  This review firstly explores the current legislative environment for AI research that uses patient image datasets and then summarises key ethical considerations that are raised on review of AI burns care research. Finally, a basic framework for the reporting of burns image datasets that are used for AI research is suggested. It is hoped that this work will contribute to wider legislative discussions with more stakeholders.

                  Drug Mechanism of Action Retrieval using Neurosymbolic Path Finding

                    Date: 2nd June 2023
                    Time: 14:00 - 16:00

                    Title: Drug Mechanism of Action Retrieval using Neurosymbolic Path Finding
                    Speakers: Lauren DeLong

                    Researchers I will be collaborating with have discovered novel plant-based drug compounds that possess some therapeutic effect. However, they do not yet understand how these compounds achieve such effects, otherwise known as the drug's mechanism of action (MOA). Uncovering drug MOAs is non-trivial: while some drug compounds act directly upon a cellular component which induces some therapeutic effect, many others act indirectly through a series of interactions. Therefore, the goal of my internship project is to use neurosymbolic AI on a biomedical knowledge graph to unveil the most likely paths by which these compounds execute their MOAs. By revealing the most likely MOAs, my method will assist the drug discovery process by facilitating a deeper understanding of: (1) how directly or indirectly a given drug compound achieves some therapeutic effect,  (2) what additional therapeutic or adverse effects can be expected from usage of said compound, and (3) how such effects may vary across cell and tissue types.

                    Charting the Landscape of Neuro-symbolic Reasoners

                      Date: 2nd June 2023
                      Time: 14:00 - 16:00

                      Title: Charting the Landscape of Neuro-symbolic Reasoners
                      Speakers: Xuelong An

                      In the first half of my presentation, I want to share an ongoing work on building a comprehensive benchmark to empirically assess the plethora of neuro-symbolic models. We note that in recent years, interest over this family of models is growing, evidenced by the constant influx of novel methods and benchmarks to test their robust generalization and reasoning capabilities. However, much of the successes reported by neuro-symbolic methods over assessed datasets are often disparate with respect to one another. There lacks a unified, comprehensive test to assess the panorama of neuro-symbolic models. To design such benchmark, we survey the current landscape of neuro-symbolic architectures and benchmarks. From this, we propose a general taxonomy for classifying current and future neuro-symbolic models and reasoning benchmarks, which helps us understand how they relate to each other. Henceforth, we propose SaSSY-CLEVR, a heterogeneous benchmark suite which can serve as a common testing ground for different neuro-symbolic reasoners to compare their strengths and limitations.

                      If time allows, in the second half of my presentation, I will share a series of experiments to assess NeSy models on CLEVR-Hans3, which test for the ability of object-centric reasoning adopted in SaSSY-CLEVR. In our study, we expand on work done by Stammer et. al (2021),  where we test the robustness of a traditional convolutional neural networks (CNN) and Neuro-Symbolic (NeSy) architectures comprising of a Slot Attention and a Set Transformer component. We evaluate different NeSy variants by comparing their classification accuracy after fine-tuning them to a modified version of the CLEVR-Hans3 dataset containing four different kinds of data complications. We find that models using the pretrained Slot Attention maintained good classification performance across data complications, indicating that the object-centric representations built by this perceptual component are crucial for model robustness. We also find that a Slot Attention with fully connected layers, instead of a Set Transformer, had the best overall performance, underscoring the importance of controlled comparisons.