The smarty4covid dataset and knowledge base: a framework enabling interpretable analysis of audio signals

Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially during COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,665), deep breathing (4,695) and voice (4,291) as recorded by means of mobile devices following a crowd-sourcing approach. Other self reported information is also included (e.g. COVID-19 virus tests), thus providing a comprehensive dataset for the development of COVID-19 risk detection models. The smarty4covid dataset is released in the form of a web-ontology language (OWL) knowledge base enabling data consolidation from other relevant datasets, complex queries and reasoning. It has been utilized towards the development of models able to: (i) extract clinically informative respiratory indicators from regular breathing records, and (ii) identify cough, breath and voice segments in crowd-sourced audio recordings. A new framework utilizing the smarty4covid OWL knowledge base towards generating counterfactual explanations in opaque AI-based COVID-19 risk detection models is proposed and validated.Comment: Submitted for publication in Nature Scientific Dat

What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project

[EN] Background To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. Methods The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. Results Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with "attractive" visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. Conclusions By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care.The research leading to these results has received funding from the European Commission under the European Union's Seventh Framework Programme (FP7/2007-2013) grant agreement no 600914.Fico, G.; Hernandez, L.; Cancela, J.; Dagliati, A.; Sacchi, L.; Martinez-Millana, A.; Posada, J.... (2019). What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? 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EPMA-World Congress 2015: Bonn, Germany. 3-5 September 2015

Table of contents A1 Predictive and prognostic biomarker panel for targeted application of radioembolisation improving individual outcomes in hepatocellular carcinoma Jella-Andrea Abraham, Olga Golubnitschaja A2 Integrated market access approach amplifying value of “Rx-CDx” Ildar Akhmetov A3 Disaster response: an opportunity to improve global healthcare Russell J. Andrews, Leonidas Quintana A4 USA PPPM: proscriptive, profligate, profiteering medicine-good for 1 % wealthy, not for 99 % unhealthy Russell J. Andrews A5 The role of IDO in a murine model of gingivitis: predictive and therapeutic potentials Babak Baban, Jun Yao Liu, Xu Qin, Tailing Wang, Mahmood S. Mozaffari A6 Specific diets for personalised treatment of diabetes type 2 Viktoriia V. Bati, Tamara V. Meleshko, Olga B. Levchuk, Nadiya V. Boyko A7 Towards personalized physiotherapeutic approach Joanna Bauer, Ewa Boerner, Halina Podbielska A8 Cells, animal, SHIME and in silico models for detection and verification of specific biomarkers of non-communicable chronic diseases Alojz Bomba, Viktor O. Petrov, Volodymyr G. Drobnych, Rostyslav V. Bubnov, Oksana M. Bykova, Nadiya V. Boyko A9 INTERACT-chronic care model: Self-treatment by patients with decision support e-Health solution Hans-Peter Brunner-La Rocca, Lutz Fleischhacker, Olga Golubnitschaja, Frank Heemskerk, Thomas Helms, Tiny Jaarsma, Judita Kinkorova, Jan Ramaekers, Peter Ruff, Ivana Schnur, Emilio Vanoli, Jose Verdu A10 PPPM in cardiovascular medicine in 2015 Hans-Peter Brunner-La Rocca A11 Magnetic resonance imaging of nanoparticles in mice, potential for theranostic and contrast media development – pilot results Rostyslav V. Bubnov, Sergiy A. Grabovetskyi, Olena M. Mykhalchenko, Natalia O. Tymoshok, Oleksandr B. Shcherbakov, Igor P. Semeniv, Mykola Y. Spivak A12 Ultrasound diagnosis for diabetic neuropathy - comparative study Rostyslav V. Bubnov, Tetyana V. Ostapenko A13 Ultrasound for stratification patients with diabetic foot ulcers for prevention and personalized treatment - pilot results Rostyslav V. Bubnov, Nazarii M. Kobyliak, Nadiya M. Zholobak, Mykola Ya. Spivak A14 Project ImaGenX – designing and executing a questionnaire on environment and lifestyle risk of breast cancer John Paul Cauchi A15 Genomics – a new structural brand of predictive, preventive and personalized medicine or the new driver as well? Dmitrii Cherepakhin, Marina Bakay, Artem Borovikov, Sergey Suchkov A16 Survey of questionnaires for evaluation of the quality of life in various medical fields Barbara Cieślik, Agnieszka Migasiewicz, Maria-Luiza Podbielska, Markus Pelleter, Agnieszka Giemza, Halina Podbielska A17 Personalized molecular treatment for muscular dystrophies Sebahattin Cirak A18 Secondary mutations in circulating tumour DNA for acquired drug resistance in patients with advanced ALK + NSCLC Marzia Del Re, Paola Bordi, Valentina Citi, Marta Palombi, Carmine Pinto, Marcello Tiseo, Romano Danesi A19 Recombinant species-specific FcεRI alpha proteins for diagnosis of IgE-mediated allergies in dogs, cats and horses Lukas Einhorn, Judit Fazekas, Martina Muhr, Alexandra Schoos, Lucia Panakova, Ina Herrmann, Krisztina Manzano-Szalai, Kumiko Oida, Edda Fiebiger, Josef Singer, Erika Jensen-Jarolim A20 Global methodology for developmental neurotoxicity testing in humans and animals early and chronically exposed to chemical contaminants Arpiné A. Elnar, Nadia Ouamara, Nadiya Boyko, Xavier Coumoul, Jean-Philippe Antignac, Bruno Le Bizec, Gauthier Eppe, Jenny Renaut, Torsten Bonn, Cédric Guignard, Margherita Ferrante, Maria Liusa Chiusano, Salvatore Cuzzocrea, Gerard O'Keeffe, John Cryan, Michelle Bisson, Amina Barakat, Ihsane Hmamouchi, Nasser Zawia, Anumantha Kanthasamy, Glen E. Kisby, Rui Alves, Oscar Villacañas Pérez, Kim Burgard, Peter Spencer, Norbert Bomba, Martin Haranta, Nina Zaitseva, Irina May, Stéphanie Grojean, Mathilde Body-Malapel, Florencia Harari, Raul Harari, Kristina Yeghiazaryan, Olga Golubnitschaja, Vittorio Calabrese, Christophe Nemos, Rachid Soulimani A21 Mental indicators at young people with attributes hypertension and pre-hypertension Maria E. Evsevyeva, Elena A. Mishenko, Zurida V. Kumukova, Evgeniy V. Chudnovsky, Tatyana A. Smirnova A22 On the approaches to the early diagnosis of stress-induced hypertension in young employees of State law enforcement agencies Maria E. Evsevyeva, Ludmila V. Ivanova, Michail V. Eremin, Maria V. Rostovtseva A23 Сentral aortic pressure and indexes of augmentation in young persons in view of risk factors Maria E. Evsevyeva, Michail V. Eremin, Vladimir I. Koshel, Oksana V. Sergeeva, Nadesgda M. Konovalova A24 Breast cancer prediction and prevention: Are reliable biomarkers in horizon? Shantanu Girotra, Olga Golubnitschaja A25 Flammer Syndrome and potential formation of pre-metastatic niches: A multi-centred study on phenotyping, patient stratification, prediction and potential prevention of aggressive breast cancer and metastatic disease Olga Golubnitschaja, Manuel Debald, Walther Kuhn, Kristina Yeghiazaryan, Rostyslav V. Bubnov, Vadym M. Goncharenko, Ulyana Lushchyk, Godfrey Grech, Katarzyna Konieczka A26 Innovative tools for prenatal diagnostics and monitoring: improving individual pregnancy outcomes and health-economy in EU Olga Golubnitschaja, Jan Jaap Erwich, Vincenzo Costigliola, Kristina Yeghiazaryan, Ulrich Gembruch A27 Immunohistochemical assessment of APUD cells in endometriosis Vadym M. Goncharenko, Vasyl O. Beniuk, Olga V. Kalenska, Rostyslav V. Bubnov A28 Updating personalized management algorithm of endometrial hyperplasia in pre-menopause women Vadym M. Goncharenko, Vasyl O. Beniuk, Rostyslav V. Bubnov, Olga Melnychuk A29 The personified treatment approach of polimorbid patients with periodontal inflammatory diseases Irina A. Gorbacheva, Lyudmila Y. Orekhova, Vadim V. Tachalov A30 Ukrainian experience in hybrid war – the challenge to update algorithms for personalized care and early prevention of different military injuries Olena I. Grechanyk, Rizvan Ya. Abdullaiev, Rostyslav V. Bubnov A31 Tear fluid biomarkers: a comparison of tear fluid sampling and storage protocols Suzanne Hagan, Eilidh Martin, Ian Pearce, Katherine Oliver A32 The correlation of dietary habits with gingival problems during menstruation Cenk Haytac, Fariz Salimov, Servin Yoksul, Anatoly A. Kunin, Natalia S. Moiseeva A33 Genomic medicine in a contemporary Spanish population of prostate cancer: our experience Bernardo Herrera-Imbroda, Sergio del Río-González, Maria Fernanda Lara, Antonia Angulo, Francisco Javier Machuca Santa-Cruz A34 Challenges, opportunities and collaborations for personalized medicine applicability in uro-oncological disease Bernardo Herrera-Imbroda, Sergio del Río-González, Maria Fernanda Lara A35 Metabolic hallmarks of cancer as targets for a personalized therapy John Ionescu A36 Influence of genetic polymorphism as a predictor of the development of periodontal disease in patients with gastric ulcer and 12 duodenal ulcer Alfiya Z. Isamulaeva, Anatoly A. Kunin, Shamil Sh. Magomedov, Aida I. Isamulaeva A37 Challenges in diabetic macular edema Tatjana Josifova A38 Overview of the EPMA strategies in laboratory medicine relevant for PPPM Marko Kapalla, Juraj Kubáň, Olga Golubnitschaja, Vincenzo Costigliola A39 EPMA initiative for effective organization of medical travel: European concepts and criteria Vincenzo Costigliola, Marko Kapalla, Juraj Kubáň, Olga Golubnitschaja A40 Design and innovation in e-textiles: implications for PPPM Anthony Kent, Tom Fisher, Tilak Dias A41 Biobank in Pilsen as a member of national node BBMRI_CZ Judita Kinkorová, Ondřej Topolčan A42 Big data in personalized medicine: hype and hope Matthias Kohl A43 The 3P approach as the platform of the European Dentistry Department (DPPPD) Anatoly A. Kunin, Natalia S. Moiseeva A44 The endometrium cytokine patterns for predictive diagnosis of proliferation severity and cancer prevention Andrii I. Kurchenko, Vasyl A. Beniuk, Vadym M. Goncharenko, Rostyslav V. Bubnov, Nadiya V. Boyko, Andriy M. Strokan A45 A monocyte-based in-vitro system for testing individual responses to the implanted material: future for personalized implant construction Julia Kzhyshkowska, Alexandru Gudima, Ksenia S. Stankevich, Victor D. Filimonov4, Harald Klüter, Evgeniya M. Mamontova, Sergei I. Tverdokhlebov A46 Prediction and prevention of adverse health effects by meteorological factors: Biomarker patterns and creation of a device for self-monitoring and integrated care Ulyana B. Lushchyk, Viktor V. Novytskyy, Igor P. Babii, Nadiya G. Lushchyk, Lyudmyla S. Riabets, Ivanna I. Legka A47 Targeting "disease signatures" towards personalized healthcare Mira Marcus-Kalish, Alexis Mitelpunkt, Tal Galili, Neta Shachar, Yoav Benjamini A48 Influence of the skin imperfection on the personal quality of life and possible tools for objective diagnosis Agnieszka Migasiewicz, Markus Pelleter, Joanna Bauer, Ewelina Dereń, Halina Podbielska A49 The new direction in caries prevention based on the ultrastructure of dental hard tissues and filling materials Natalia S. Moiseeva, Anatoly A. Kunin, Dmitry A. Kunin A50 The use of LED radiation in prevention of dental diseases Natalia S. Moiseeva, Yury A. Ippolitov, Dmitry A. Kunin, Alexei N. Morozov, Natalia V. Chirkova, Nakhid T. Aliev A51 Status of endothelial progenitor cells in diabetic nephropathy: predictive and preventive potentials Mahmood S. Mozaffari, Jun Yao Liu, Babak Baban A52 The status of glucocorticoid-induced leucine zipper protein in salivary gland in Sjögren’s syndrome: predictive and personalized treatment potentials Mahmood S. Mozaffari, Jun Yao Liu, Rafik Abdelsayed, Xing-Ming Shi, Babak Baban A53 Maximal aerobic capacity - important quality marker of health Jaroslav Novák, Milan Štork, Václav Zeman A54 The EMPOWER project: laboratory medicine and Horizon 2020 Wytze P. Oosterhuis, Elvar Theodorsson A55 Personality profile manifestations in patient’s attitude to oral care and adherence to doctor’s prescriptions Lyudmila Y. Orekhova, Tatyana V. Kudryavtseva, Elena R. Isaeva, Vadim V. Tachalov, Ekaterina S. Loboda A56 Results of an European survey on personalized medicine addressed to directions of laboratory medicine Mario Pazzagli, Francesca Malentacchi, Irene Mancini, Ivan Brandslund, Pieter Vermeersch, Matthias Schwab, Janja Marc, Ron H.N. van Schaik, Gerard Siest, Elvar Theodorsson, Chiara Di Resta A57 MCI or early dementia predictive speech based diagnosis techniques Matus Pleva, Jozef Juhar A58 Personalized speech based mobile application for eHealth Matus Pleva, Jozef Juhar A59 Circulating tumor cell-free DNA as the biomarker in the management of cancer patients Jiří Polívka jr., Filip Janků, Martin Pešta, Jan Doležal, Milena Králíčková, Jiří Polívka A60 Complex stroke care – educational programme in Stroke Centre University Hospital Plzen Jiří Polívka, Alena Lukešová, Nina Müllerová, Petr Ševčík, Vladimír Rohan A61 Sleep apnea and sleep fragmentation contribute to brain aging Kneginja Richter, Lence Miloseva, Günter Niklewski A62 Personalised approach for sleep disturbances in shift workers Kneginja Richter, Jens Acker, Guenter Niklewski A63 Medical travel and innovative PPPM clusters: new concept of integration Olga Safonicheva, Vincenzo Costigliola A64 Medical travel and women health Olga Safonicheva A65 Continuity of generations in the training of specialists in the field of reconstructive microsurgery Maxim Sautin, Janna Sinelnikova, Sergey Suchkov A66 Telemonitoring of stroke patients – empirical evidence of individual risk management results from an observational study in Germany Songül Secer, Stephan von Bandemer A67 Women’s increasing breast cancer risk with n-6 fatty acid intake explained by estrogen-fatty acid interactive effect on DNA damage: implications for gender-specific nutrition within personalized medicine Niva Shapira A68 Cytobacterioscopy of the gingival crevicular fluid as a method for preventive diagnosis of periodontal diseases Aleksandr Shcherbakov, Anatoly A. Kunin, Natalia S. Moiseeva A69 Use of specially treated composites in dentistry to avoid violations of aesthetics Bogdan R. Shumilovich, Zhanna Lipkind, Yulia Vorobieva, Dmitry A. Kunin, Anastasiia V. Sudareva A70 National eHealth system – platform for preventive, predictive and personalized diabetes care Ivica Smokovski, Tatjana Milenkovic A72 The common energy levels of Prof. Szent-Györgyi, the intrinsic chemistry of melanin, and the muscle physiopathology. Implications in the context of Preventive, Predictive, and Personalized Medicine Arturo Solís-Herrera, María del Carmen Arias-Esparza, Sergey Suchkov A73 Plurality and individuality of hepatocellular carcinoma: PPPM perspectives Krishna Chander Sridhar, Olga Golubnitschaja A74 Strategic aspects of higher medical education reforms to secure newer educational platforms for getting biopharma professionals matures Maria Studneva, Sihong Song, James Creeden, Мark Мandrik, Sergey Suchkov A75 Overview of the strategies and activities of the European Federation of Clinical Chemistry and Laboratory Medicine, (EFLM) Elvar Theodorsson, EFLM A76 New spectroscopic techniques for point of care label free diagnostics Syed A. M. Tofail A77 Tumor markers for personalized medicine and oncology - the role of Laboratory Medicine Ondřej Topolčan, Judita Kinkorová, Ondřej Fiala, Marie Karlíková, Šárka Svobodová, Radek Kučera, Radka Fuchsová, Vladislav Třeška, Václav Šimánek, Ladislav Pecen, Jan Šoupal, Štěpán Svačina2 A78 Modern medical terminology (MMT) as a driver of the global educational reforms Evgeniya Tretyak, Maria Studneva, Sergey Suchkov A79 Juvenile hypertension; the relevance of novel predictive, preventive and personalized assessment of its determinants Francesca M. Trovato, G. Fabio Martines, Daniela Brischetto, Daniela Catalano, Giuseppe Musumeci, Guglielmo M. Trovato A80 Proteomarkers Biotech George Th. Tsangaris, Athanasios K. Anagnostopoulos A81 Proteomics and mass spectrometry based non-invasive prenatal testing of fetal health and pregnancy complications George Th. Tsangaris, Athanasios K. Anagnostopoulos A82 Integrated Ecosystem for an Integrated Care model for Heart Failure (HF) patients including related comorbidities (ZENITH) José Verdú, German Gutiérrez, Jordi Rovira, Marta Martinez, Lutz Fleischhacker, Donna Green, Arthur Garson, Elena Tamburini, Stefano Cuomo, Juan Martinez-Leon, Teresa Abrisqueta, Hans-Peter Brunner-La Rocca, Tiny Jaarsma, Teresa Arredondo, Cecilia Vera, Giuseppe Fico, Olga Golubnitschaja, Fernando Arribas, Martina Onderco, Isabel Vara, on behalf of ZENITH consortium A83 Predictive, preventive and personalized medicine in diabetes onset and complication (MOSAIC project) José Verdú, Francesco Sambo, Barbara Di Camillo, Claudio Cobelli, Andrea Facchinetti, Giuseppe Fico, Riccardo Bellazzi, Lucia Sacchi, Arianna Dagliati, Daniele Segnani, Valentina Tibollo, Manuel Ottaviano, Rafael Gabriel, Leif Groop, Jacqueline Postma, Antonio Martinez, Liisa Hakaste, Tiinamaija Tuomi, Konstantia Zarkogianni, on behalf of MOSAIC consortium A84 Possibilities for personalized therapy of diabetes using in vitro screening of insulin and oral hypoglycemic agents Igor Volchek, Nina Pototskaya, Andrey Petrov A85 The innovative technology for personalized therapy of human diseases based on in vitro drug screening Igor Volchek, Nadezhda Pototskaya, Andrey Petrov A86 Bone destruction and temporomandibular joint: predictive markers, pathogenetic aspects and quality of life Ülle Voog-Oras, Oksana Jagur, Edvitar Leibur, Priit Niibo, Triin Jagomägi, Minh Son Nguyen, Chris Pruunsild, Dagmar Piikov, Mare Saag A87 Sub-optimal health management – global vision for concepts in medical travel Wei Wang A88 Sub-optimal health management: synergic PPPM-TCAM approach Wei Wang A89 Innovative technologies for minimal invasive diagnostics Andreas Weinhäusel, Walter Pulverer, Matthias Wielscher, Manuela Hofner, Christa Noehammer, Regina Soldo, Peter Hettegger, Istvan Gyurjan, Ronald Kulovics, Silvia Schönthaler, Gabriel Beikircher, Albert Kriegner, Stephan Pabinger, Klemens Vierlinger A90 Rare disease diobanks for personalized medicine Ayşe Yüzbaşıoğlu, Meral Özgüç, Member of EuroBioBank - European Network of DNA, Cell and Tissue Banks for Rare Disease

Intelligent personalized medical decision support systems for the management of diabetes mellitus

The scope of the present thesis is the design, development and evaluation of intelligent medical decision support systems, aiming at optimizing the treatment of patients with Diabetes Mellitus (DM). Specifically, within the framework of the present thesis, several methods have been developed for the analysis and processing of data related to medical electronic health records, laboratory measurements and continuous glucose and insulin records, towards the design and the development of: i) an intelligent Insulin Infusion Advisory System (IIAS), able to provide real time estimations of the appropriate insulin infusion rates for type 1 DM patients using continuous glucose monitors and insulin pumps (Artificial Pancreas), in order to maintain glucose levels within the physiological range, and ii) models for the risk assessment of long-term complications of Type I and Type II DM, focusing on diabetic retinopathy. In the first part of this study, a simulation model of the glucose - insulin metabolism system of Type I DM has been developed, based on the combined use of compartmental models (CMs) and Neural Networks (NNs). The model is incorporated into a Model Predictive Controller (MPC) in order for the latter to estimate insulin infusion rates. In order to evaluate the performance of the IIAS, several in silico trials have been performed. Moreover, a clinical trial has been conducted under controlled conditions, the results of which provided valuable input for SSEI improvement. In particular, a tuning algorithm based on fuzzy logic has been developed, for the on line adaptation of the NMPC parameters. The enhanced SSEI has been tested against its ability to handle meal disturbances, fasting conditions, delays, noise to the glucose measurements, interpatient variability, and erroneous meal size estimations. The second part of the present thesis, concerns the development of models able to estimate the risk of people with Type I or Type II DM to develop over time, long-term diabetes complications, with particular focus to diabetic retinopathy. Towards this direction, advanced classification techniques are applied based on the wavelet neural networks. For the development and the evaluation of the aforementioned medical decision support systems, data from Type I and Type II DM patients, have been used, acquired from the Department of Pediatrics, Diabetes Center, P&A Kyriakoy Children’s Hospital, Athens and the Athens Hippokration Hospital.Στην παρούσα διατριβή σχεδιάζονται, αναπτύσσονται και αξιολογούνται ευφυή συστήματα υποστήριξης εξατομικευμένων ιατρικών αποφάσεων που στοχεύουν στη βελτιστοποίηση της θεραπείας των ατόμων με Σακχαρώδη Διαβήτη (ΣΔ). Συγκεκριμένα, οι μέθοδοι που αναπτύσσονται χρησιμοποιούνται για την ανάλυση και την επεξεργασία δεδομένων Ηλεκτρονικού Ιατρικού Φακέλου, Εργαστηριακών Μετρήσεων καθώς και συνεχών καταγραφών γλυκόζης και ινσουλίνης, με σκοπό i) τη σχεδίαση και ανάπτυξη Συμβουλευτικού Συστήματος Έγχυσης Ινσουλίνης (ΣΣΕΙ), το οποίο εκτιμά σε πραγματικό χρόνο τον απαιτούμενο ρυθμό έγχυσης ινσουλίνης σε άτομα με ΣΔ Τύπου Ι, που χρησιμοποιούν Διάταξη Συνεχούς Μέτρησης Γλυκόζης (ΔΣΜΓ) και αντλία έγχυσης ινσουλίνης («Τεχνητό Πάγκρεας»), ώστε τα επίπεδα γλυκόζης αίματος, να διατηρούνται εντός φυσιολογικών ορίων και ii) την ανάπτυξη μοντέλων αποτίμησης της πιθανότητας εμφάνισης μακροπρόθεσμων επιπλοκών του ΣΔ Τύπου Ι και Τύπου ΙΙ, εστιάζοντας στη διαβητική αμφιβληστροειδοπάθεια. Στο πρώτο μέρος της διατριβής εφαρμόζονται προηγμένες μέθοδοι μοντελοποίησης, που βασίζονται στη συνδυασμένη χρήση Διαμερισματικών Μοντέλων (ΔΜ) και Νευρωνικών Δικτύων (ΝΔ) για την προσομοίωση του μεταβολικού συστήματος γλυκόζης-ινσουλίνης σε άτομα με ΣΔ Τύπου Ι. Το τελικό μοντέλο ενσωματώνεται σε έναν ελεγκτή που βασίζεται σε μοντέλο πρόβλεψης (Model Predictive Control-MPC), για τον μετέπειτα υπολογισμό των συνιστώμενων ρυθμών έγχυσης ινσουλίνης. Για τον λεπτομερή έλεγχο ορθής λειτουργίας του ΣΣΕΙ, πραγματοποιήθηκε σειρά υπολογιστικών πειραμάτων. Επιπλέον, διεξήχθη κλινική δοκιμή σε νοσοκομείο υπό ελεγχόμενες συνθήκες, τα αποτελέσματα της οποίας ανέδειξαν αδυναμίες του ΣΣΕΙ και οδήγησαν στη βελτίωσή του. Συγκεκριμένα, αναπτύχθηκε προσαρμοστικός αλγόριθμος αυτόματης και σε πραγματικό χρόνο, ενημέρωσης των παραμέτρων του ελεγκτή χρησιμοποιώντας τεχνικές ασαφούς λογικής. Το βελτιωμένο ΣΣΕΙ εξετάστηκε ως προς την ικανότητά του να διαχειρίζεται διαταραχές γευμάτων, καταστάσεις νηστείας, καθυστερήσεις, ανακρίβειες στις μετρήσεις γλυκόζης, διαφορές στο μεταβολισμό γλυκόζης που υφίστανται μεταξύ ατόμων με ΣΔ Τύπου Ι (inter-patient variability), καθώς και λανθασμένες εκτιμήσεις της περιεχόμενης ποσότητας των υδατανθράκων στα λαμβανόμενα γεύματα. Το δεύτερο μέρος της εργασίας αφορά στην ανάπτυξη μοντέλων εκτίμησης της πιθανότητας ατόμων με ΣΔ Τύπου Ι και Τύπου ΙΙ να εμφανίσουν σε βάθος χρόνου μακροπρόθεσμες επιπλοκές του ΣΔ, εστιάζοντας στη διαβητική αμφιβληστροειδοπάθεια. Για το σκοπό αυτό, εφαρμόστηκαν τεχνικές ταξινόμησης των δεδομένων, με χρήση τεχνητών νευρωνικών δικτύων με κυματιδιακές συναρτήσεις ενεργοποίησης. Για την ανάπτυξη και την αξιολόγηση των συστημάτων χρησιμοποιήθηκαν ιατρικά δεδομένα ατόμων με ΣΔ Τύπου Ι και Τύπου ΙΙ, που παραχωρήθηκαν από την Α’ Παιδιατρική Κλινική, Διαβητολογικό Κέντρο του Νοσοκομείου Π & Α Κυριακού, καθώς και από το Διαβητολογικό Κέντρο του Ιπποκράτειου Νοσοκομείου Αθηνών

Intelligent Personalized Medical Decision Support Systems for the Management of Diabetes Mellitus

182 σ.Στην παρούσα διατριβή σχεδιάζονται, αναπτύσσονται και αξιολογούνται ευφυή συστήματα υποστήριξης εξατομικευμένων ιατρικών αποφάσεων που στοχεύουν στη βελτιστοποίηση της θεραπείας των ατόμων με Σακχαρώδη Διαβήτη (ΣΔ). Συγκεκριμένα, οι μέθοδοι που αναπτύσσονται χρησιμοποιούνται για την ανάλυση και την επεξεργασία δεδομένων Ηλεκτρονικού Ιατρικού Φακέλου, Εργαστηριακών Μετρήσεων καθώς και συνεχών καταγραφών γλυκόζης και ινσουλίνης, με σκοπό i) τη σχεδίαση και ανάπτυξη Συμβουλευτικού Συστήματος Έγχυσης Ινσουλίνης (ΣΣΕΙ), το οποίο εκτιμά σε πραγματικό χρόνο τον απαιτούμενο ρυθμό έγχυσης ινσουλίνης σε άτομα με ΣΔ Τύπου Ι, που χρησιμοποιούν Διάταξη Συνεχούς Μέτρησης Γλυκόζης (ΔΣΜΓ) και αντλία έγχυσης ινσουλίνης («Τεχνητό Πάγκρεας»), ώστε τα επίπεδα γλυκόζης αίματος, να διατηρούνται εντός φυσιολογικών ορίων και ii) την ανάπτυξη μοντέλων αποτίμησης της πιθανότητας εμφάνισης μακροπρόθεσμων επιπλοκών του ΣΔ Τύπου Ι και Τύπου ΙΙ, εστιάζοντας στη διαβητική αμφιβληστροειδοπάθεια. Στο πρώτο μέρος της διατριβής εφαρμόζονται προηγμένες μέθοδοι μοντελοποίησης, που βασίζονται στη συνδυασμένη χρήση Διαμερισματικών Μοντέλων (ΔΜ) και Νευρωνικών Δικτύων (ΝΔ) για την προσομοίωση του μεταβολικού συστήματος γλυκόζης-ινσουλίνης σε άτομα με ΣΔ Τύπου Ι. Το τελικό μοντέλο ενσωματώνεται σε έναν ελεγκτή που βασίζεται σε μοντέλο πρόβλεψης (Model Predictive Control-MPC), για τον μετέπειτα υπολογισμό των συνιστώμενων ρυθμών έγχυσης ινσουλίνης. Για τον λεπτομερή έλεγχο ορθής λειτουργίας του ΣΣΕΙ, πραγματοποιήθηκε σειρά υπολογιστικών πειραμάτων. Επιπλέον, διεξήχθη κλινική δοκιμή σε νοσοκομείο υπό ελεγχόμενες συνθήκες, τα αποτελέσματα της οποίας ανέδειξαν αδυναμίες του ΣΣΕΙ και οδήγησαν στη βελτίωσή του. Συγκεκριμένα, αναπτύχθηκε προσαρμοστικός αλγόριθμος αυτόματης και σε πραγματικό χρόνο, ενημέρωσης των παραμέτρων του ελεγκτή χρησιμοποιώντας τεχνικές ασαφούς λογικής. Το βελτιωμένο ΣΣΕΙ εξετάστηκε ως προς την ικανότητά του να διαχειρίζεται διαταραχές γευμάτων, καταστάσεις νηστείας, καθυστερήσεις, ανακρίβειες στις μετρήσεις γλυκόζης, διαφορές στο μεταβολισμό γλυκόζης που υφίστανται μεταξύ ατόμων με ΣΔ Τύπου Ι (inter-patient variability), καθώς και λανθασμένες εκτιμήσεις της περιεχόμενης ποσότητας των υδατανθράκων στα λαμβανόμενα γεύματα. Το δεύτερο μέρος της εργασίας αφορά στην ανάπτυξη μοντέλων εκτίμησης της πιθανότητας ατόμων με ΣΔ Τύπου Ι και Τύπου ΙΙ να εμφανίσουν σε βάθος χρόνου μακροπρόθεσμες επιπλοκές του ΣΔ, εστιάζοντας στη διαβητική αμφιβληστροειδοπάθεια. Για το σκοπό αυτό, εφαρμόστηκαν τεχνικές ταξινόμησης των δεδομένων, με χρήση τεχνητών νευρωνικών δικτύων με κυματιδιακές συναρτήσεις ενεργοποίησης. Για την ανάπτυξη και την αξιολόγηση των συστημάτων χρησιμοποιήθηκαν ιατρικά δεδομένα ατόμων με ΣΔ Τύπου Ι και Τύπου ΙΙ, που παραχωρήθηκαν από την Α’ Παιδιατρική Κλινική, Διαβητολογικό Κέντρο του Νοσοκομείου Π & Α Κυριακού, καθώς και από το Διαβητολογικό Κέντρο του Ιπποκράτειου Νοσοκομείου Αθηνών.The scope of the present thesis is the design, development and evaluation of intelligent medical decision support systems, aiming at optimizing the treatment of patients with Diabetes Mellitus (DM). Specifically, within the framework of the present thesis, several methods have been developed for the analysis and processing of data related to medical electronic health records, laboratory measurements and continuous glucose and insulin records, towards the design and the development of: i) an intelligent Insulin Infusion Advisory System (IIAS), able to provide real time estimations of the appropriate insulin infusion rates for type 1 DM patients using continuous glucose monitors and insulin pumps (Artificial Pancreas), in order to maintain glucose levels within the physiological range, and ii) models for the risk assessment of long-term complications of Type I and Type II DM, focusing on diabetic retinopathy. In the first part of this study, a simulation model of the glucose - insulin metabolism system of Type I DM has been developed, based on the combined use of compartmental models (CMs) and Neural Networks (NNs). The model is incorporated into a Model Predictive Controller (MPC) in order for the latter to estimate insulin infusion rates. In order to evaluate the performance of the IIAS, several in silico trials have been performed. Moreover, a clinical trial has been conducted under controlled conditions, the results of which provided valuable input for SSEI improvement. In particular, a tuning algorithm based on fuzzy logic has been developed, for the on line adaptation of the NMPC parameters. The enhanced SSEI has been tested against its ability to handle meal disturbances, fasting conditions, delays, noise to the glucose measurements, interpatient variability, and erroneous meal size estimations. The second part of the present thesis, concerns the development of models able to estimate the risk of people with Type I or Type II DM to develop over time, long-term diabetes complications, with particular focus to diabetic retinopathy. Towards this direction, advanced classification techniques are applied based on the wavelet neural networks. For the development and the evaluation of the aforementioned medical decision support systems, data from Type I and Type II DM patients, have been used, acquired from the Department of Pediatrics, Diabetes Center, P&A Kyriakoy Children’s Hospital, Athens and the Athens Hippokration Hospital.Κωνσταντία Χ. Ζαρκογιάνν

An explainable XGBoost-based approach towards assessing the risk of cardiovascular disease in patients with Type 2 Diabetes Mellitus

Cardiovascular Disease ( CVD) is an important cause of disability and death among individuals with Diabetes Mellitus (DM). International clinical guidelines for the management of Type 2 DM (T2DM) are founded on primary and secondary prevention and favor the evaluation of CVD-related risk factors towards appropriate treatment initiation. CVD risk prediction models can provide valuable tools for optimizing the frequency of medical visits and performing timely preventive and therapeutic interventions against CVD events. The integration of explainability modalities in these models can enhance human understanding on the reasoning process, maximize transparency and embellish trust towards the models’ adoption in clinical practice. The aim of the present study is to develop and evaluate an explainable personalized risk prediction model for the fatal or non-fatal CVD incidence in T2DM individuals. An explainable approach based on the eXtreme Gradient Boosting (XGBoost) and the Tree SHAP (SHapley Additive exPlanations) method is deployed for the calculation of the 5-year CVD risk and the generation of individual explanations on the model’s decisions. Data from the 5- year follow up of 560 patients with T2DM are used for development and evaluation purposes. The obtained results (AUC=71.13%) indicate the potential of the proposed approach to handle the unbalanced nature of the used dataset, while providing clinically meaningful insights about the model’s decision process

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations

A Multimodal Approach for Real Time Recognition of Engagement towards Adaptive Serious Games for Health

In this article, an unobtrusive and affordable sensor-based multimodal approach for real time recognition of engagement in serious games (SGs) for health is presented. This approach aims to achieve individualization in SGs that promote self-health management. The feasibility of the proposed approach was investigated by designing and implementing an experimental process focusing on real time recognition of engagement. Twenty-six participants were recruited and engaged in sessions with a SG that promotes food and nutrition literacy. Data were collected during play from a heart rate sensor, a smart chair, and in-game metrics. Perceived engagement, as an approximation to the ground truth, was annotated continuously by participants. An additional group of six participants were recruited for smart chair calibration purposes. The analysis was conducted in two directions, firstly investigating associations between identified sitting postures and perceived engagement, and secondly evaluating the predictive capacity of features extracted from the multitude of sources towards the ground truth. The results demonstrate significant associations and predictive capacity from all investigated sources, with a multimodal feature combination displaying superiority over unimodal features. These results advocate for the feasibility of real time recognition of engagement in adaptive serious games for health by using the presented approach

An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays