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Manifold Theory in Isabelle/HOL

    Date: 23rd February 2024
    Time: 14:00 - 16:00

    Title: Manifold Theory in Isabelle/HOL
    Speakers: Richard Schmoetten

    I will give a whirlwind tour of my work in the formalisation of manifolds in Isabelle/HOL, and I will show how to get around the restrictions of simple types to formalise interesting properties of vector fields. To motivate doing all this theory, I will preface my talk with a selection of example applications of manifolds.


    Understanding and Modelling Activities of Daily Living for People in Later Life

      Date: 1st December 2023
      Time: 14:00 - 15:00

      Title: Understanding and Modelling Activities of Daily Living for People in Later Life
      Speakers: Ricardo Contreras

      Activities of Daily Living (ADLs) are tasks that people perform on a day-to-day basis to cover essential (physical) needs. How these activities are performed can be quite complex and change according to the environment/subject context, or the way in which the activities themselves are accomplished.  Establishing whether an ADL is performed as expected allows for the identification of deviations and the (potential) creation of interventions.  These aspects contribute to the best quality of life for people in later years.  In this talk I will present the work we have conducted as part of the ACRC programme.  I will start with the introduction of the ADLs.  I will delve into the extraction and symbolic encoding of properties (using linear temporal logic) related to identified ADLs, followed by an introduction to a rigorous and systematic approach (model checking) to surface deviations of these properties.  Then I will describe the main components of our model, show our initial results (based on real data) and present the conclusions and future work.


      Integrating Knowledge Graph Data with Large Language Models for Explainable Inference

        Date: 6th October 2023
        Time: 15:00 - 16:00

        Title:  Integrating Knowledge Graph Data with Large Language Models for Explainable Inference
        Speakers: Carlos Efraín Quintero Narvaez

        Recent advancements in Large Language Models (LLMs) such as OpenAI GPT, BERT, and LLaMA have demonstrated their potential for complex reasoning tasks with natural language. However, there is still room for improvement as it is costly to train these models to work with specialized data, and their inner workings are not yet fully understood. In this line of thought, Neurosymbolic Artificial Intelligence, which combines Symbolic Logic Reasoning and Deep Learning, aims to create explainable inference models using the virtues of the two fields. Knowledge Graphs (KGs) are an essential component in this subject, since they provide concise representations of large knowledge bases, understandable for both users and models. Two significant challenges in this area are query answering from KGs, and the integration of KG information into the output of language models. To address these issues, researchers have proposed various approaches, including the use of Deep Learning for complex queries on KGs and Augmented Language Models that integrate recognition of entities from a KG. In this thesis, we propose to modify and combine these approaches with recent LLM developments, creating an explainable way for LLMs to work with data from any KG. Our approach will use LLMs for the KG entity embedding steps utilized in existing techniques, while keeping the other parts of the methods intact. Furthermore, we will use this new querying method for executing KG queries in the internal inference process of LLMs. Using an architecture that integrates entity embeddings to the model’s inference. Our goal is to reduce the frequency of hallucinations and enhance the coherency of LLMs by allowing them to provide informed explanations, making them more broadly useful for general contexts.

        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).