Data4SmartHealth 2022

Smart Data Factory

An event to develop an AI-focused and data-driven e-health community, based at the NOI techpark in Bolzano.

NOI Techpark , Seminar Room 4

<h1>Description</h1>EN: The third (in-person and online) edition of Data4SmartHealth aims to promote knowledge and awareness of artificial intelligence-based methodologies and techniques for analyzing clinical data and building health informatics applications. The workshop is dedicated to physicians and other healthcare professionals, staff of ICT companies working in the field of medical informatics, scholars who wish to deepen their knowledge on the topic, and any other interested people. Attendees of the event will receive an overview of some of the key issues and most promising techniques in the field through four tutorials given by experts in e-health, artificial intelligence and data analytics. In continuity with previous editions, the goal of the workshop is also to consolidate the development of a community, centered on the NOI techpark in Bolzano, committed to the dissemination of the use of digital technologies in healthcare and the joint implementation of research and development projects.IT: La terza edizione (di persona e online) di Data4SmartHealth mira a promuovere la conoscenza e la consapevolezza delle metodologie e delle tecniche basate sull'intelligenza artificiale per l'analisi dei dati clinici e la costruzione di applicazioni informatiche sanitarie. Il workshop è dedicato a medici e altri professionisti del settore sanitario, personale di aziende ICT che operano nel campo dell'informatica medica, studiosi che desiderano approfondire le proprie conoscenze sull'argomento e qualsiasi altra persona interessata. I partecipanti all'evento riceveranno una panoramica di alcune delle principali problematiche e delle tecniche più promettenti del settore attraverso quattro tutorial tenuti da esperti di e-health, intelligenza artificiale e data analytics. In continuità con le precedenti edizioni, l'obiettivo del workshop è anche quello di consolidare lo sviluppo di una comunità, incentrata sul NOI techpark di Bolzano, impegnata nella diffusione dell'uso delle tecnologie digitali in sanità e nella realizzazione congiunta di progetti di ricerca e sviluppo.DE: Die dritte (persönliche und Online-) Ausgabe von Data4SmartHealth zielt darauf ab, das Wissen und das Bewusstsein für auf künstlicher Intelligenz basierende Methoden und Techniken zur Analyse klinischer Daten und zur Entwicklung von Gesundheitsinformatik-Anwendungen zu fördern. Der Workshop richtet sich an Ärzte und andere Fachkräfte des Gesundheitswesens, Mitarbeiter von IKT-Unternehmen, die im Bereich der medizinischen Informatik tätig sind, Wissenschaftler, die ihr Wissen zu diesem Thema vertiefen möchten, und alle anderen Interessierten. Die Teilnehmer der Veranstaltung erhalten in vier Tutorien, die von Experten für elektronische Gesundheitsdienste, künstliche Intelligenz und Datenanalyse gehalten werden, einen Überblick über einige der wichtigsten Themen und vielversprechendsten Techniken auf diesem Gebiet. Wie schon bei früheren Veranstaltungen ist das Ziel des Workshops auch die Konsolidierung der Entwicklung einer Gemeinschaft im NOI Techpark in Bozen, die sich für die Verbreitung der Nutzung digitaler Technologien im Gesundheitswesen und die gemeinsame Durchführung von Forschungs- und Entwicklungsprojekten einsetzt.____________________________________________________________________________________

An event organized by<h3><a href="https://smart.inf.unibz.it/en" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">S﻿mart Data Factory</a>, Faculty of Computer Science, Free University of Bozen-Bolzano</h3>i﻿n cooperation with<h3><a href="https://aixia.it/en/gruppi/hc/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">AI-HCare</a> working group of the Italian Association for Artificial Intelligence (AIxIA)</h3>____________________________________________________________________________________

<h1>Program</h1>T﻿he talks will be in Italian or English at the speaker's choice / Gli interventi saranno in italiano o in inglese a scelta del relatore.8:20 - 8:50 Registration 8:50 - 9:00 Welcome<ul><li><a href="https://www.unibz.it/en/faculties/computer-science/academic-staff/person/36376-claus-pahl" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Prof. Claus Pahl</a>, Dean of Faculty of Computer Science, Free University of Bozen-Bolzano</li><li><a href="https://www.unibz.it/it/faculties/computer-science/academic-staff/person/29416-floriano-luca-zini" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Dr. Floriano Zini</a>, Smart Data Factory - Free University of Bozen-Bolzano and <a href="https://pdi.fbk.eu/ghidini/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Dr. Chiara Ghidini</a>, Fondazione Bruno Kessler and vice-president of Italian Association for Artificial Intelligence (AIxIA)</li></ul>9:00 - 9:50 Tutorial 1<ul><li>Towards smarter health care: can Artificial Intelligence help? (<a href="https://www.unimib.it/fabio-antonio-stella" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Prof. Fabio Stella</a>, Università degli Studi di Milano Bicocca) (<a href="https://drive.google.com/file/d/1HTaNb7typc6Jd0a7Fi3U_5JopA7XYZeG/view?usp=share_link" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">slides</a>)</li></ul>9:55 - 10:45 Tutorial 2<ul><li>A bird-eye-view of process mining (with a focus on healthcare) (<a href="https://www.unibz.it/it/faculties/computer-science/academic-staff/person/31326-marco-montali" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Prof. Marco Montali</a> and <a href="https://www.unibz.it/en/faculties/computer-science/academic-staff/person/41895-fabrizio-maria-maggi" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Prof. Fabrizio Maria Maggi</a>, Free University of Bozen-Bolzano) (<a href="https://drive.google.com/file/d/1eTQfM6vWDTmgIEwlZC3GjYRHS2hHqYlZ/view?usp=share_link" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">slides</a>)</li></ul>10:45 - 11:15 Coffee break @<a href="https://g.page/noisteria_techpark?share" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Noisteria</a>11:15 -12:05 Tutorial 3<ul><li>AI in Medicine: a case study in clinical neuroscience (<a href="https://www.linkedin.com/in/paolo-avesani-6895182/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">D﻿r. Paolo Avesani</a><a href="https://www.di.univr.it/?ent=persona&amp;id=7391" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"></a><a href="https://www.di.univr.it/?ent=persona&amp;id=7391" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"></a>, Fondazione Bruno Kessler) (<a href="https://drive.google.com/file/d/1OgXjpIoy2VpYFSVW5QtpLJDYQoC1Xs7J/view?usp=share_link" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">slides</a>)</li></ul>12:10 -13:00 Tutorial 4 <ul><li>Computational Persuasion for Behavior Change in Healthcare (<a href="https://www.unibz.it/en/faculties/computer-science/academic-staff/person/45237-ivan-donadello" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Dr. Ivan Donadello</a>, Free University of Bozen-Bolzano) (<a href="https://drive.google.com/file/d/1rSGgo7zzjqbhKtOvyuL8_A3fa950tkwS/view?usp=share_link" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">slides</a>) </li></ul>13:00 - 13:10 Closing<ul><li><a href="https://www.unibz.it/it/faculties/computer-science/academic-staff/person/29416-floriano-luca-zini" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Dr. Floriano Zini</a>, Smart Data Factory - Free University of Bozen-Bolzano and <a href="https://pdi.fbk.eu/ghidini/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Dr. Chiara Ghidini</a>, Fondazione Bruno Kessler and vice-president of Italian Association for Artificial Intelligence (AIxIA)</li></ul>____________________________________________________________________________________

<h1>Event video</h1><h3><a href="https://drive.google.com/file/d/1Svxe8t1jvPhoRgi_J24AvvFX4yJeIJpS/view" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">F﻿irst part</a></h3><h3><a href="https://drive.google.com/file/d/1JYiKJ6A7Ks108zojutjeBAK4eLSY3qgw/view" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">S﻿econd part</a></h3><a href="https://scientificnet.zoom.us/j/93570158700?pwd=aWFTOUJTdFRtaEpUTTVSZ3RBKzBmdz09" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"></a>____________________________________________________________________________________

<h1>Tutorial abstracts</h1><a href="https://www.unimib.it/fabio-antonio-stella" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Prof. Fabio Stella</a>, Università degli Studi di Milano BicoccaTowards smarter health care: can Artificial Intelligence help?Artificial intelligence, machine learning and deep learning are introduced together with their capabilities and main limitations. The ladder of causation, i.e., the hierarchy of artificial intelligence tasks proposed by Judea Pearl is introduced and discussed. The three rungs of the ladder, namely, seeing, intervening and counterfactuals, are exploited to clarify which research questions can be answered depending on the type of the available data and on the available domain/expert knowledge. We show that prediction is not the same as decision making, and that knowing the story behind the data is fundamental to make effective and explainable decisions. This is achieved by presenting and discussing elementary examples where ignoring the story behind the data directly brings to non-sense conclusions. We conclude by illustrating some examples where causal Bayesian networks have been developed to help clinicians and physicians to reason under uncertainty and to make decisions.Speaker's short bio: Fabio Stella is an associate professor at the University of Milan-Bicocca, Department of Informatics, Systems and Communication, where he leads the model and algorithms for data and text mining research lab. His main research interests are Bayesian networks and causal networks for health care, biology and finance. He has been the Principal Investigator of several research projects funded by private companies in the health care sector. He published more than 100 papers, and has been awarded the 10% best reviewers at NeurIPS in 2020 and the outstanding reviewer award at NeurIPS, AISTATS and ICML in 2022.(<a href="https://drive.google.com/file/d/1HTaNb7typc6Jd0a7Fi3U_5JopA7XYZeG/view?usp=share_link" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">slides</a>)____________________________________________________________________________________<a href="https://www.unibz.it/it/faculties/computer-science/academic-staff/person/31326-marco-montali" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Prof. Marco Montali</a> and <a href="https://www.unibz.it/en/faculties/computer-science/academic-staff/person/41895-fabrizio-maria-maggi" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Prof. Fabrizio Maria Maggi</a>, Free University of Bozen-BolzanoA bird-eye-view of process mining (with a focus on healthcare)Process mining is a discipline at the intersection of data science, business process management and computing, whose ultimate goal is to support organizations in the continuous improvement of their processes based on actual facts rather than fiction. This is obtained through the analysis of data produced during the execution of such processes. Of particular relevance are event data, tracing which process instances have been executed and which relevant events occurred therein. Each event typically comes with key information pointing to reference process instance, the related activity in the process, the execution timestamp, together with additional relevant attributes (such as the resource who generated the event). Process mining relates such event data to process (and other organizational) models, making it possible to discover process models from data (thus describing what actually happened, and not what should happen), to check conformance of actual and expected behaviors, and to enhance models with extracted information, highlighting frequent paths, inefficiencies, and bottlenecks.Process mining techniques are of particular relevance in highly complex, unstructured, and unpredictable organizational contexts, such as that of healthcare. The goal of this tutorial is to provide a bird-eye view of process mining and illustrate the main process mining functionalities and techniques. To do so, we will use an event log from containing events of sepsis cases from a hospital.Speakers' short bio: Fabrizio Maria Maggi in an associate professor at the Faculty of Computer Science, Free University of Bozen-Bolzano. His areas of research span process modeling and process analysis. He has coauthored articles on process mining, automated revision of business process models, declarative business process modeling, monitoring of business constraints at runtime, service oriented architectures, service choreographies, and service composition.Marco Montali is a full professor and vice-dean for studies at the Faculty of Computer Science, Free University of Bozen-Bolzano. He develops novel, foundational and applied techniques grounded in artificial intelligence, logics, and formal methods, to create intelligent agents and information systems that combine processes and data, with a particular interest in the combination of model-driven and data-driven techniques. He is co-author of more than 230 papers, many of which in top-tier conferences and journals, and recipient of 10 best paper awards and 2 test-of-time awards.(<a href="https://drive.google.com/file/d/1eTQfM6vWDTmgIEwlZC3GjYRHS2hHqYlZ/view?usp=share_link" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">slides</a>)____________________________________________________________________________________<a href="https://www.linkedin.com/in/paolo-avesani-6895182/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">D﻿r. Paolo Avesani</a><a href="https://www.di.univr.it/?ent=persona&amp;id=7391" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"></a>, Fondazione Bruno KesslerAI in Medicine: a case study in clinical neuroscienceThe recent innovation in brain neuroimaging is going to have a disruptive impact in the diagnosis and treatment of brain disorders. The novel techniques allow the characterization of functional and structural brain networks at the individual level. The common neurosurgical practices are going to be revised accordingly. This new scenario poses new challenges both for clinicians and artificial intelligence. While a data driven approach to neuroimaging is opening new opportunities, we have to not neglect potential pitfalls and computational biases. We present the experience of adoption of advanced computational methods in the Clinical Neuroscience Department at S. Chiara Hospital in Trentino.Speaker's short bio: Paolo Avesani is the head of the Neuroinformatics Lab (NILab) raised as a joint initiative of the Center for Digital Health of Fondazione Bruno Kessler and the Center for Mind/Brain Sciences of the University of Trento. His research interests include the data-driven computational methods for the characterization of functional and structural brain connectivity.(<a href="https://drive.google.com/file/d/1OgXjpIoy2VpYFSVW5QtpLJDYQoC1Xs7J/view?usp=share_link" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">slides</a>)____________________________________________________________________________________<a href="https://www.unibz.it/en/faculties/computer-science/academic-staff/person/45237-ivan-donadello" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Dr. Ivan Donadello</a>, Free University of Bozen-BolzanoComputational Persuasion for Behavior Change in Healthcare Virtual coaches (VCs) are computer programs (mobile phone apps or chatbots) that simulate conversations with people by mimicking a human being. Their aim is to support and help users in performing a particular task. They have become an increasingly relevant resource in healthcare for the management of chronic diseases and for promoting behavioral changes in the self-management of individuals. For example, a growing number of studies report their effectiveness in reducing perceived stress and abnormal eating behavior, improving symptoms of anxiety, depression and in coping with diabetes mellitus. In this tutorial, we will present a promising field of Artificial Intelligence for developing VCs: the Computational Persuasion. This provides VCs of smart (and tailored to users) dialogue strategies to persuade users at adhering medical prescriptions and advice. We will see the main techniques underlying Computational Persuasion as well as some important results in Behavior Change for Healthcare.Speaker's short bio: Ivan Donadello is a researcher at the Faculty of Computer Science, Free University of Bozen-Bolzano. His current research interest mainly focuses on virtual agents (or softbots) able to recognize the health state of a person and give suggestions to improve it. His expertise encompasses the fields of Knowledge Representation (ontologies and Fuzzy Logic), Machine/Deep Learning, Computer Vision, eHealth and Explainable Artificial Intelligence.(<a href="https://drive.google.com/file/d/1rSGgo7zzjqbhKtOvyuL8_A3fa950tkwS/view?usp=share_link" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">slides</a>)____________________________________________________________________________________

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Data4SmartHealth 2022

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