“Health & AI” Workshop ENIA IAFER Project
10 February, 2025
We are pleased to announce the organisation of the free Workshop ‘Health and AI’, on the increasing importance that Artificial Intelligence, AI, plays in Health. This activity is part of the Artificial Intelligence, Ethical, Responsible and General Purpose (IAFER) project.
It will be held in person at the UGR-AI Building in the PTS on Tuesday 18 February 2025, 10-12:30h and online at the following link:
Only the first 50 registrants will be able to attend in person, the rest will be able to access online. The speakers are specialists in the use of AI for complex medical problems from the Universities of Granada, Nottingham, and Imperial College London.
See below for the seminar agenda – don’t forget to register!
10:00-10:30 Responsible Artificial Intelligence for the Afterlife
Abstract: Artificial intelligence is revolutionising how we interact with the world, and death is no exception. During this talk, Prof. Perez will present the UKRI research programme RAi UK https://rai.ac.uk/ , a £35M investment developed to deliver world-leading best practices for how to design, evaluate, regulate, and operate AI-systems in ways that benefit people and society. The talk then explores a specific case study: DeathTech, (i.e., emerging technologies that aim to preserve the memories of our loved ones digitally). From creating conversational avatars to reconstructing voices and faces using generative AI, the possibilities seem limitless. However, it’s crucial to approach this with responsibility. The talk examines how AI can help us cope with grief and keep the memories of our loved ones alive, while emphasising the need for a robust ethical framework to ensure these technologies are used for the benefit of society.
Biography: Elvira Perez Vallejos is Professor of Mental Health and Technology, jointly appointed by the School of Medicine and the School of Computer Science at the University of Nottingham. She holds a highly interdisciplinary research portfolio (>£80M), backed by funding from major UK research councils (AHRC, EPSRC, ESRC, MRC) and the National Institute for Health Research (NIHR). She is the Chair of the Equities Committee at UKRI Responsible AI UK and Director of Responsible Research and Innovation at UKRI Trustworthy Autonomous Systems Hub. Elvira is at the forefront of global research on digital mental health. Her work addresses critical issues such as data ethics, privacy, user and stakeholder engagement, co-production, and responsible AI. Over the past six years, she has collaborated with young people to amplify their voices and translate their concerns and recommendations into policy recommendation in the field of digital mental health. This work contributed to the UK Law: Online Safety Act 2023. Currently, her research work focuses on ‘Responsible AI for Death and Dying’ and how emerging digital afterlife immortality services (e.g., griefbots, deadbots and postmortem avatars) may affect the process of grief and mourning.
10:30-11:00 Subgrouping Germinal Center-Derived B-Cell Lymphomas based on Machine Learning-deduced DNA Methylation Modules
Abstract: Existing subgrouping techniques for diffuse large B-cell lymphomas based on morphology, transcriptomics, or genetic alterations are hindered by overlapping molecular signatures, intratumoral heterogeneity, and inconsistent reproducibility. Although DNA methylation profiling has successfully stratified solid tumors and leukemias, its application to mature B-cell lymphomas (FL and DLBCL) is challenged by a continuous rather than discrete distribution of methylation states. To address this, we use an unsupervised framework that integrates preliminary DNA methylation clustering with fuzzy non-negative matrix factorization (FNMF) to extract robust, interpretable methylation markers. This approach is able to capture inherent ambiguities in methylation patterns, thereby enhancing subtype delineation. Results revealed 300 CpGs forming four methylation modules, which ordered the lymphomas into seven methylation patterns (MP1-7). These MP1-7 showed significant associations with biological features of the lymphomas and were replicated in external samples.
Biography: María Coral del Val Muñoz is Associate Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada. She teaches in several degrees, including the Degree in Biotechnology, the Degree in Dentistry and the Master’s Degrees in Data Science and Computer Engineering and in Molecular Biology Applied to Biotechnology Companies. She obtained her PhD in 2000 at the University of Granada with the thesis entitled ‘Diversity of arbuscular mycorrhizal fungi in soils contaminated with heavy metals’, supervised by Dr. Concepción Azcón González de Aguilar. After his PhD, he did postdoctoral stays at the Spanish National Research Council (CSIC) in Granada and at the German Cancer Research Centre (DKFZ) in Heidelberg, Germany. During this period, she collaborated with Molecular Biophysics departments and Bioinformatics groups, broadening her experience in genomic analysis and bioinformatics. His research work focuses on areas such as Bioinformatics, Intelligent Systems and Artificial Intelligence. He has participated in projects related to genomic data analysis and the application of artificial intelligence techniques in biology and medicine. In addition to her teaching and research activity, Professor del Val is a member of the research group ‘Soft Computing and Intelligent Information Systems’ at the University of Granada.
11:30-12:00 Enhancing the analysis of Idiopathic Pulmonary Fibrosis through Longitudinal FVC Trajectories and Endotype Identification Using Advanced Computational Techniques
Abstract: Idiopathic pulmonary fibrosis (IPF) patient clusters were identified through sensitivity analysis enhanced machine learning (ML) models. Two studies were conducted: 1) analyzing FVC trajectories and 2) classifying endotypes. In FVC analysis, RF models trained with MCMC simulations showed the lowest NRMSD. Four FVC clusters were obtained, each associated with distinct mortality risks. A significant FVC decline in the first-year post-diagnosis indicated higher mortality risk. For endotype classification, three clusters – Basement Membrane, Epithelial Injury, and Crosslinked Fibrin – were identified, each associated with unique survival rates and biomarker profiles. Sensitivity analysis and replication in an independent dataset confirmed these results. Conclusions: Sensitivity analysis enhances ML models by improving their robustness, interpretability, and generalisability in analysing longitudinal FVC trajectories and endotype classification in IPF. These findings support distinct lung function trajectories and endotypes, each with unique clinical characteristics, and offer insights into IPF progression for future clinical trials and improved patient management.
Biography: Hernán Fainberg is a scientist with over 20 years of experience, whose career integrates human physiology, evolution, statistics, and computational algorithms for data processing. His biomedical research focuses on advancing therapeutic solutions in fields such as early childhood development and age-related diseases, particularly idiopathic pulmonary fibrosis (IPF). He collaborates with leading scientists from prestigious institutions across Argentina, Israel, Saudi Arabia, Kuwait, Australia, the United States, Spain (University of Granada), France, and the United Kingdom. His research has been published in high-impact, peer-reviewed journals, including The Lancet Respiratory Medicine, Trends in Molecular Medicine, and JCI Insight. In addition to his expertise in data analysis, Hernán has practical experience in molecular biology, bacteriology, and animal physiology laboratories, giving him a broad perspective on scientific challenges. This interdisciplinary background enables him to approach data analysis with an integrated and holistic perspective. Currently, he is a Senior Research Fellow at the Margaret Turner Warwick Centre for Fibrosing Lung Disease at Imperial College London. His work focuses on clustering analysis of blood biomarkers to identify molecular profiles in conditions such as pulmonary fibrosis, linking them to physiological and clinical factors such as lung capacity. This approach aims to classify patients into specific endotypes, contributing to the advancement of personalised medicine. In his recent research, Hernán has applied sensitivity analysis to overcome the “black-box” effect of machine learning, improving the interpretability of complex models. This has opened new perspectives for understanding heterogeneous biological data and developing innovative biomedical solutions, leading to more precise interpretations of information from various databases.
12:00-12:30 Multi-Species and Multi-Antibiotic Resistance Classification with MALDI-TOF
Abstract: Antimicrobial Resistance (AMR) poses a significant global health threat, impacting clinical treatments, agriculture, and public health. Although mass spectrometry techniques like MALDI-TOF provide opportunity for rapid AMR detection, state-of-the-art models are largely based on manual analysis or classic machine learning techniques that are heavily dependent on preprocessing. In this talk, we will discuss MSDeepAMR, a convolutional network that predicts AMR from the raw mass spectrometry data. The model was tested against several antibiotics and bacterium species, highlighting the accuracy variability in each case. The model was also tested under transfer learning conditions, to estimate its performance when dealing with data from small hospitals. Furthermore, we discuss an adaptation of MSDeepAMR to incorporate multi-species and multi-antibiotic classification, enabling simultaneous prediction of resistance across multiple antibiotics and bacterial species. Results demonstrate enhanced prediction accuracy and generalizability, highlighting the potential of integrating multi-label classification with MALDI-TOF data for efficient and scalable multidrug AMR detection.
Data controller: University of Granada and the Andalusian Inter-University Institute of Data Science and Computational Intelligence (DaSCI). The University of Granada is entitled to process your personal data because it is necessary for the performance of a task carried out in the public interest or in the exercise of public powers vested in the data controller: art. 6.1 e) RGPD. The purpose of the processing is to process your application for registration. Recipients: University of Granada, for registration on the virtual platform. Rights: You have the right to request access, opposition, rectification, deletion, limitation or portability of the processing of your data, as explained in the additional information.
Biography: Daniel Peralta Cámara is a Permanent Lecturer in the Department of Computer Science and Artificial Intelligence at the University of Granada. He teaches at the Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación. In the academic field, he teaches subjects in the Degree in Computer Engineering, specifically ‘Design and Development of Information Systems’. He also participates in the Master’s Degree in Computer Engineering, where he teaches ‘Planning and Management of Computer Projects’ as part of the Project Management module. As for his research work, Dr. Peralta Cámara is part of the research group ‘Soft Computing and Intelligent Information Systems’. In addition to his work at the University of Granada, he collaborates internationally with the Vlaams Instituut voor Biotechnologie, thus extending his research network and contributing to the advancement of his field of specialisation.
This training is part of the project ‘Ethical, Responsible and General Purpose Artificial Intelligence: Applications in Risk Scenarios (IAFER)’ Exp.: TSI-100927-2023-1 funded through the creation of university-industry chairs (Enia Chairs), aimed at research and development of artificial intelligence, for its dissemination and training in the framework of the European Recovery, Transformation and Resilience Plan, funded by the European Union-Next Generation EU.
