Italy

Cancer patients may struggle with mental wellbeing issues such as distress and depression. As a part of the CAPABLE project, we aim to develop a digital behaviour-change intervention that helps them build positive health habits and improve their wellbeing. The main challenge to the evaluation of the system is the lack of access to real data prior to intervention start. Therefore, first, we created a simulator that mimics patient responses to activity suggestions based on Fogg’s behaviour model. Later we used supervised and reinforcement learning methods to learn the best time of sending the patient prompts. We found that the reinforcement learning methods learn quickly not to over-notify patients and find prompt policies that are more effective in facilitating users in performing target activity than a random notification strategy, but are less effective than adaptive supervised learning method trained to predict patient responsiveness.

AIxIA 2021

From Personalized Timely Notification to Healthy Habit Formation: A Feasibility Study of Reinforcement Learning Approaches on Synthetic Data

workshop paper

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The 20th International Conference of the Italian Association for Artificial Intelligence (**AIxIA 2021**) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

 
 



**General Chair**

Stefania Bandini, University of Milano-Bicocca, Italyand RCAST - The University of Tokyo, Japan


**Program Chairs**

Viviana Mascardi, University of Genova, Italy; Matteo Palmonari, University of Milano-Bicocca, Italy; Giuseppe Vizzari, University of Milano-Bicocca, Italy

**Organizing Committee**

Francesca Gasparini, University of Milano-Bicocca, Italy (**Chair**); Stefano Ferilli, University of Bari, Italy; Marta Giltri, University of Milano-Bicocca, Italy; Daniela Briola, University of Insubria, Italy


**Publicity and Communication Committee**

Elisabetta Fersini, University of Milano-Bicocca, Italy (**Chair**); Angelo Ferrando, University of Genova, Italy; Francesca Alessandra Lisi, University of Bari, Italy; Giovanni Stilo, University of L'Aquila, Italy; Marco Viviani, University of Milano-Bicocca, Italy


**Workshop Chairs**

Gabriella Cortellessa, ISTC-CNR, Rome, Italy; Silvia Rossi, University of Naples Federico II, Italy; Fabio Stella, University of Milano-Bicocca, Italy


**PC Members**

Fulvio Mastrogiovanni, University of Genova, Italy; Roberto Basili, University of Rome Tor Vergata, Italy; Nicola Gatti, Polytechnic University of Milan, Italy; Stefano Bistarelli, University of Perugia, Italy; Piero Poccianti, Consorzio Operativo Gruppo MPS, Italy; Francesco Scarcello, University of Calabria, Italy; Flavio S. Correa Da Silva, University of São Paulo, Brazil; Elena Cabrio, Université Côte d'Azur, CNRS, Inria, I3S, France; Antonio Lieto, University of Turin, Italy; Diego Calvanese, Free University of Bozen-Bolzano, Italy; Alessio Micheli, University of Pisa, Italy; Francesca A. Lisi University of Bari Aldo Moro, Italy; Mauro Vallati, University of Huddersfield, United Kingdom; Bernardo Magnini, Fondazione Bruno Kessler (FBK), Italy; Luigia C. Aiello, Sapienza University of Rome, Italy; Rafael H. Bordini, Pontifical Catholic University of Rio Grande do Sul, Brazil; Chiara Ghidini, Fondazione Bruno Kessler (FBK), Italy; Federico Bergenti, University of Parma, Italy; Francesco Ricca, University of Calabria, Italy; Andrea Burattin, Technical University of Denmark, Denmark; Angelo Oddi, ISTC-CNR, Italian National Research Council, Italy; Farshad Badie, Aalborg University, Denmark; Emanuel Sallinger, University of Oxford, United Kingdom; Marin Lujak, University Rey Juan Carlos, Spain; Giancarlo Fortino, University of Calabria, Italy; Salvatore Gaglio, University of Palermo, Italy; Johannes K. Fichte, TU Dresden, Germany; Matteo Baldoni, University of Turin, Italy; Fabrizio Riguzzi, Univeristy of Ferrara, Italy; Ivana Dusparic, Trinity College Dublin, Ireland; Serena Villata, CNRS - Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, France; Paolo Rosso, Technical University of Valencia, Spain; Ana L. C. Bazzan, Federal University of Rio Grande do Sul, Brazil; Gianluigi Greco, University of Calabria, Italy; Stefano Cagnoni, University of Parma, Italy; Gian Luca Pozzato, University of Turin, Italy; Salvatore Ruggieri, University of Turin, Italy; Claudio Di Ciccio, Sapienza University of Rome, Italy; Eloisa Vargiu, Cetaqua, Water Technology Center, Spain; Marco Maratea, University of Genova, Italy; Andrea Roli, University of Bologna, Italy; Viviana Patti, University of Turin, Italy; Stefania Costantini, University of L'Aquila, Italy; Antonius Weinzierl, Vienna University of Technology, Austria; Silvia Rossi, University of Naples Federico II, Italy; Giovanni Semeraro, University of Bari, Italy; Luciano Serafini, Fondazione Bruno Kessler (FBK), Italy; Laura Giordano, University of Eastern Piedmont, Italy; Ernesto Jimenez-Ruiz, City, University of London, United Kingdom; Marco Gavanelli, University of Ferrara, Italy; Federico Chesani, University of Bologna, Italy

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The 20th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2021) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

The interest in Explainable Artificial Intelligence (XAI) research is dramatically grown during the last few years. The main reason is the need of having systems that beyond being effective are also able to describe how a certain output has been obtained and to present such a description in a comprehensive manner with respect to the target users. A promising research direction making black boxes more transparent is the exploitation of semantic information. Such information can be exploited from different perspectives in order to provide a more comprehensive and interpretable representation of AI models. In this paper, we focus on one of the key components of the semantic-based explanation generation process: the explanation graph. We discuss its role and how it can work has a bridge for making an explanation more understandable by the target users, complete, and privacy preserving. We show how the explanation graph can be integrated into a real-world solution and we discuss challenges and future work.

Bridging Signals to Natural Language Explanations With Explanation Graphs

We aim to develop a virtual coaching (VC) system which facilitates cancer patients with the formation of positive health habits. The VC should adjust content and delivery of recommendations based on user’s preferences. We conducted a survey that elicited potential VC users’ perceptions about the appropriate context for notification, aspects driving their motivation, and their ability to perform a selected activity in different circumstances. We analyzed 173 survey responses, identified different types of users and formulated suggestions how to adjust application content and notification timing to increase their susceptibility to non-medication health interventions.

Personality and habit formation: Is there a link?

AI applications to health related processes include the adoption of robotic platforms for rehabilitation, aiming at the delivery of highly intensive, repeatable and accurate motion therapies and able to constantly monitor the patient and provide the suitable assistance levels. However, a comprehensive approach to robot-aided rehabilitation requires also a social level of interaction with the patient that implies cognitive modeling and linguistic communication. It is worth noticing that robotic platforms providing both physical and cognitive support to patients have not been proposed so far. In the HeAL9000 project an intelligent robot for rehabilitation of patients affected by musculoskeletal disorders is proposed with cognitive and linguistic abilities. It relies strongly on machine learning technologies whose aim is to support cost effective engineering of the platform as well as evolving capabilities across time. In the paper early experimental evidence is acquired through quantitative evaluation.

HeAL9000: an Intelligent Rehabilitation Robot

In this paper we propose an early stage decision support system for personalized arteriovenous fistula (AVF) management. The goal of the model is to identify an optimal strategy to recognize the onset of a stenosis and intervene to prevent the failure of the AVF. We used an Influence Diagram (ID) that combines a risk model, clinical tests, angioplasty and searches a series of policies that optimizes the cost of treatment.

Personalized arterovenous fistula management through utility maximization with Influence Diagram

Scientific progress is offering increasingly better ways to tailor a patient’s treatment to the patient’s needs, i.e., better support for optimal clinical decision-making can be offered. Choosing the appropriate treatment for a patient depends on numerous factors, including pathology results, tumor stage, genetic, and molecular characteristics. Bayesian networks are a type of probabilistic artificial intelligence, which in principle would be suitable to support complex clinical decision-making. However, most clinicians do not have experience with these networks. This paper describes an approach of developing a clinical decision support system based on Bayesian networks, that does not require insight knowledge about the underlying computational model for its use. It is developed as a therapy-oriented approach with a focus on usability and explainability. The approach features the computation and presentation of individualized treatment recommendations, comparison of treatments and patient cases, as well as explanations and visualizations providing additional information on the current patient case.

User-centered Development of a Clinical Decision Support System

In a context of digitalization and modernization of healthcare, automatic analysis of clinical data plays a leading role in improving the quality of care. Since much of the information lies in an unstructured form within clinical notes, it is necessary to make use of modern Natural Language Processing techniques to extract and build structured knowledge from the data. However, clinical texts pose unique challenges due to the extensive usage of i) acronyms, ii) non-standard medical jargons and iii) typos over technical terms. In this paper, we present a prototype spell-checker specifically designed for medical texts written in Italian.

A support for understanding medical notes, correcting spelling errors in Italian clinical records

The development of microarray devices has led to the accumulation of DNA microarray datasets. Through this technological advance, physicians are able to examine various aspects of gene expression for cancer diagnosis. As data accumulation rapidly increases, the task of machine learning faces considerable challenges for high-dimensional DNA microarray data classification. Gene selection is a popular and powerful approach to deal with these high-dimensional cancer data. In this paper, a novel graph clustering-based gene selection approach is developed. The developed approach has two main objectives, consisting of relevance maximization and redundancy minimization of the selected genes. In this method, in each iteration, one subgraph is extracted, and then among the existing genes in this cluster, appropriate genes are selected using filter-based measure. The reported results on five cancer datasets indicate that the developed gene selection approach can improve the accuracy of cancer diagnosis.

Gene Selection for Cancer Diagnosis via Iterative Graph Clustering-based Approach

Towards Computational Argumentation in Healthcare

Congenital heart disease (CHD) is the most common congenital malformation and has high morbidity and mortality related to late diagnosis. Screening protocols are lacking and only 1% of murmurs are associated with CHD. The decline in auscultation skills highlights the need for better screening. This study aims to create and evaluate models for the detection of CHD using clinical data and sound features. These features were extracted using pure conventional MFCC and selected MFCC through matrix profiling and motif search. Four combinations of data were used to train decision trees (DT) and artificial neural networks (ANN), and the area under the curve (AUC) was compared. Posteriorly, models were also trained for the detection of any cardiac pathology. In both pathologies, the ANN model using clinical data and conventional MFCC showed the highest performance with AUC of 0.761 for CHD and 0.791 for any cardiac pathology. However, this is only a slight improvement when compared with the ANN models using only clinical data (0.747 and 0.789, respectively. Additionally, the inclusion of motif selected MFCC seems to worsen the model performance. Although further research is still needed, this is a potential improvement in CHD screening, particularly for primary care physicians.

Congenital Heart Disease Detection Using Clinical Data and Auscultation Heart Sounds: a Machine Learning Approach

Probabilistic graphical models, such as Bayesian networks and influence diagrams, offer coherent rep-resentation of domain knowledge under uncertainty. They are based on the sound foundations of probabil-ity theory and they readily combine available statis-tics with expert judgment. This paper describes our work in progress on a probabilistic causal model for diagnosis of liver disorders that we plan to apply in both clinical practice and medical training. The net-work, and especially its numerical parameters, is based on data from a clinical database. We present the Bayesian model and report initial results of our diagnostic performance tests.

From Personalized Timely Notification to Healthy Habit Formation: A Feasibility Study of Reinforcement Learning Approaches on Synthetic Data

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Bridging Signals to Natural Language Explanations With Explanation Graphs

From Personalized Timely Notification to Healthy Habit Formation: A Feasibility Study of Reinforcement Learning Approaches on Synthetic Data

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Bridging Signals to Natural Language Explanations With Explanation Graphs

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