Automatically monitoring and supporting healthy lifestyle is a recent research trend, fostered by the availability of low-cost monitoring devices, and it can significantly contribute to the prevention of chronic diseases deriving from incorrect diet and lack of physical activity.
Chronic diseases, such as heart disease, cancer, and diabetes, are responsible for approximately 70% of deaths among Europe and U.S. each year and they account for about 75% of the health spending1,2. Such chronic diseases can be largely preventable by eating healthy, exercising regularly, avoiding (tobacco) smoking, and receiving preventive services. Prevention at every stage of life would help people stay healthy, avoid or delay the onset of diseases, and keep diseases they already have from becoming worse or debilitating; it would also help people lead productive lives and, at the end, reduce the costs of public health.
In the last decades, health care systems in many countries have invested
substantial effort in informing people about the benefits of adopting healthy behaviors in their
lives [
However, engaging people in developing and maintaining healthier patterns of
living is a challenging task as well. To this end, generating effective personalized
recommendations implies, for example, the justification of given suggestions and the
adaptation of messages in response to the modification of the environment and of the user
status. For this reason, as opposed to hardwired persuasive features, systems that applies
general reasoning capabilities to provide flexible persuasive communication based on
rich and diverse linguistic outputs are required. In this context, modeling persuasion
mechanisms and performing flexible and context-dependent persuasive actions is more
ambitious than most current approaches on persuasive technologies (see Captology [
Explainable Artificial Intelligence (XAI) aims at explaining the algorithmic decisions
of AI solutions with non-technical terms in order to make these decision trusted and
easily understandable by humans [
In this keynote I introduced the problem of adopting XAI systems within the
healthcare domain and I discussed which are the main challenges that we need to address for
design an effective, efficient, and reliable XAI approach that can be accepted within
the healthcare domain. Then, I presented the XAI platform we realized, called
HORUS.AI [
The HORUS.AI platform is an AI-based system built upon the integration of semantic web technologies and persuasive techniques for motivating people to adopt healthy lifestyle or for supporting them to cope with the self-management of chronic diseases. The system collects data from users’ devices, explicit users’ inputs, or from the external environment (e.g. facts of the world) and interacts with users by using a goal-based metaphor. Interactive dialogues are used for proposing set of challenges to users that, through a mobile application, are able to provide the required information and to receive contextual motivational messages helping them to achieve the proposed goals.
HORUS.AI is constituted by two main layers: the Knowledge and the
DialogBased Persuasive layers. The Knowledge Layer contains the knowledge bases
modeling the specific domains for which users are monitored (e.g. diet), the rules provided
by domain-experts, and the RDF-based reasoner that combines the modeled
knowledge with the users’ generated data. The concepts and rules of healthy behaviors are
formalized as a TBox of the HeLiS ontology [
Semantic AI for Healthcare - The HORUS.AI platform her/his dietary behavior are acquired through a user’s dietary diary with the help of a smartphone application. This information populates the HeLiS ABox with logical individuals. The reasoner combines knowledge and user’s data (TBox and ABox) to infer the user behavior and generates inconsistencies if the user does not follow the rules of a healthy lifestyle. The results produced by reasoning operations are coded into motivational strategies and messages by the Dialog-based Persuasive Layer.
The Dialog-based Persuasive Layer creates and manages dialogues and generates
motivational messages based on the information provided by the Knowledge Layer and
learned from previous users’ behavior. Once an inconsistency, i.e., an unhealthy user
behavior, is detected the system shows the user a natural language message explaining
the wrong behavior and its consequences. This translation from a logic language to plain
text comprehensible by humans leverages a computational persuasion framework [
These two layers are supported by an Input/Output Layer exploited for directly communicating with users (i.e. dedicated mobile application or social media channels) by providing summaries of the acquired data, the chat containing the interactions between the users and the system, and graphical items showing the users statuses with respect to their goals. HORUS.AI has been validated within the context of different territorial labs and projects and the observed results demonstrated the suitability of HORUS.AI in real-world scenarios. In particular, I reported the validation we performed within a pilot project (named Key To Health) run into Fondazione Bruno Kessler where a mobile application linked to the HORUS.AI platform has been used by a group of 120 users for 49 days.