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Talks

Investigating associations between physical multimorbidity and subsequent depression via a systematic cluster analysis

    Date: 3rd May 2024
    Time: 14:00 - 16:00

    Title: Investigating associations between physical multimorbidity and subsequent depression via a systematic cluster analysis
    Speakers: Lauren DeLong
    Abstract:

    Multimorbidity, the co-occurrence of two or more conditions within an individual, is a growing challenge for health and care delivery as well as for research Many multimorbidity studies focus upon the co-occurrence of physical health conditions, but mental health disorders are less represented. However, recent studies have revealed links of a bidirectional nature between depression and physical conditions. To investigate associations between physical multimorbidity and subsequent depression, we first performed a clustering analysis upon baseline morbidity data for UK Biobank participants. In contrast to previous similar studies, we compared the usefulness of four independent clustering methods. The identified clusters indicated which physical conditions tend to co-occur most frequently in the whole population and stratified by sex. Finally, we used survival analysis to compare time to subsequent depression diagnosis between participants with particular groups of physical conditions at baseline and those without physical conditions at baseline. In comparison to agglomerative hierarchical clustering, latent class analysis, and k­-medoids, we found that k-modes models showed the best clustering performance amongst several metrics. Notably, the differentially represented conditions within several clusters reflected known bodily systems, such as the respiratory or digestive systems. While we found that certain clusters had stronger associations with depression, we also noted a positive correlation between such associations and the average number of conditions per participant. Therefore, both the type and number of conditions likely influence the subsequent diagnosis of depression. Our findings suggest further investigation into other factors, like social ones, which may link the effects of physical multimorbidity and depression.

     

    Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups

      Date: 3rd May 2024
      Time: 14:00 - 16:00

      Title: Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups
      Speakers: Guillermo Romero Moreno
      Abstract:

      Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data 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), a standard measure in the literature, and compared them to our proposed measure, Associations Beyond Chance (ABC). 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. Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon 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 evidence 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. In this talk, I will briefly introduce common network science approaches to multimorbidity research, present and explain our methodological approach, and present and show how our method can be easily used by the multimorbidity community via our released software package (https://github.com/Juillermo/ABC).

       

      Bayesian Multi-view Clustering for Grouping of Patients in Critical Care

        Date: 19th April 2024
        Time: 14:00 - 15:00

        Title: Bayesian Multi-view Clustering for Grouping of Patients in Critical Care
        Speakers: Luwei (Demi) Wang
        Abstract:

        The COVID-19 pandemic underscored the need to tailor medical interventions to patient characteristics by identifying at-risk subgroups. While extensive research has explored associations between pre-existing conditions and COVID-19 complications, less attention has been given to the relationship between pre-existing conditions and symptom presentation. Latent Class Analysis (LCA) is commonly employed to investigate this association. However, traditional clustering methods like LCA falter by relying solely on a single data source, amalgamating disparate data streams and potentially compromising outcome accuracy. This approach overlooks individual clinical profiles' distinctiveness. Integrating multiple data sources, such as medical history and acute symptom presentation, offers a more tailored approach. Our study aims to utilize various clustering methods to explore connections between pre-existing condition clusters and symptom clusters while preserving their uniqueness. Multi-view clustering (MVC) techniques, unlike single-view methods, leverage consensus and complementary information to enhance clustering accuracy across multiple data representations. We evaluate different MVC methods—Binary Multi-view Clustering (BMVC), Consensus Graph Learning (CGL), and Bayesian Consensus Clustering (BCC)—against our proposed Bayesian approach. Simulation studies showcase our approach's ability to uncover target clustering structures and its advantage in discovering complementary clusters. Utilizing the ISARIC dataset further elucidates distinct characteristics of these methodologies. In conclusion, the performance of MVC methods varies depending on assumptions and model implementations. Our approach offers fast processing and data-driven insights, uniquely uncovering complementary clusters compared to existing methods.

         

        Predicting long-term incidence of new-onset dementia using primary and secondary care data from Electronic Health Records

          Date: 22nd March 2024
          Time: 14:30 - 16:00

          Title: Predicting long-term incidence of new-onset dementia using primary and secondary care data from Electronic Health Records
          Speakers: Konstantin Georgiev
          Abstract:

          Dementia is a devastating and frequently life-limiting condition, which affects over 90,000 older people in Scotland. While there are no cures available, recent scientific breakthroughs suggest the potential to further slow dementia progression, but these medications are likely to be needed very early in the condition. A recent surge in studies incorporating routine data to predict incidence of dementia has aided in this risk assessment, but a lot of these are selectively biased to include people with pre-existing cognitive impairments and episodes of memory loss. In this longitudinal study, my objective is to provide an inclusive community-level risk assessment, exploring the effects of demographic and lifestyle factors linked with medical and comorbidity history on incidence of dementia. In this talk, I will share some of the key stages of developing and validating a supervised Machine Learning tool for identifying people at risk of new-onset dementia at 5 and 10 years over a large NHS Lothian population. Given the complexity of dementia, this approach is unlikely to provide perfect diagnostic performance at individual-level predictions, but even just identifying groups with substantially higher than average long-term risk can aid in controlling for these risk factors through preventative interventions and drug therapy.

           

          How to set up interventional and/or invasive clinical studies not involving investigational medicinal products in the NHS

            Date: 22nd March 2024
            Time: 14:30 - 16:00

            Title: How to set up interventional and/or invasive clinical studies not involving investigational medicinal products in the NHS
            Speakers: Fiona Smith
            Abstract:

            Ever wondered how the anonymised healthcare data sets required for machine learning research come to exist and what approvals are needed before you can start looking at the data and doing the analysis? This talk will give a brief overview of the steps involved in setting up a clinical study in the NHS that does not involve an investigational medicinal product (Non-CTIMPs). It is informed by my experience of setting up a Non-CTIMP that covers more than one NHS health board. Expect an overview of the role of The Academic and Clinical Central Office for Research and Development (ACCORD) in setting up NHS Lothian/University of Edinburgh research projects, the School of Informatics Ethics Approval process, and what is involved in writing project protocols, patient information sheets, patient consent forms, data management plans and securing data sharing agreements.

             

            Mechanising Tensors in Isabelle/HOL

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

              Title: Mechanising Tensors in Isabelle/HOL
              Speakers: Filip Smola
              Abstract:

              In this talk I will describe our ongoing effort to mechanise tensors - a generalisation of scalars, vectors and matrices - in Isabelle/HOL. This project was started by Matt as part of his master's project and builds on the work of Alexander Bentkamp (at that time of Vrije Universiteit Amsterdam).

              I will introduce what tensors are and where they are used. They have found many uses around machine learning in particular, because they are good for expressing structured data and operations on it. Then I will introduce Bentkamp's representation of tensors, highlight some of its drawbacks and show how our approach fixes those. Particularly interesting is the system we use to avoid having to redefine existing operations. Then I will close on some of our plans for to where take this project in the future.

              Manifold Theory in Isabelle/HOL

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

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

                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
                  Abstract:

                  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
                    Abstract:

                    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)
                        Abstract:
                        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)
                        Abstract:
                        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)
                        Abstract:
                        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