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Tiribelli S, Calvaresi D. Rethinking Health Recommender Systems for Active Aging: An Autonomy-Based Ethical Analysis. SCIENCE AND ENGINEERING ETHICS 2024; 30:22. [PMID: 38801621 PMCID: PMC11129984 DOI: 10.1007/s11948-024-00479-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 04/02/2024] [Indexed: 05/29/2024]
Abstract
Health Recommender Systems are promising Articial-Intelligence-based tools endowing healthy lifestyles and therapy adherence in healthcare and medicine. Among the most supported areas, it is worth mentioning active aging. However, current HRS supporting AA raise ethical challenges that still need to be properly formalized and explored. This study proposes to rethink HRS for AA through an autonomy-based ethical analysis. In particular, a brief overview of the HRS' technical aspects allows us to shed light on the ethical risks and challenges they might raise on individuals' well-being as they age. Moreover, the study proposes a categorization, understanding, and possible preventive/mitigation actions for the elicited risks and challenges through rethinking the AI ethics core principle of autonomy. Finally, elaborating on autonomy-related ethical theories, the paper proposes an autonomy-based ethical framework and how it can foster the development of autonomy-enabling HRS for AA.
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Affiliation(s)
- Simona Tiribelli
- Department of Political Sciences, Communication, and International Relations, University of Macerata, 62100, Macerata, Italy.
- Institute for Technology and Global Health, PathCheck Foundation, 955 Massachusetts Ave, Cambridge, MA, 02139, USA.
| | - Davide Calvaresi
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Rue de l'Industrie 23, 1950, Sion, Switzerland
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Buzcu B, Tessa M, Tchappi I, Najjar A, Hulstijn J, Calvaresi D, Aydoğan R. Towards interactive explanation-based nutrition virtual coaching systems. AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2024; 38:5. [PMID: 38261966 PMCID: PMC10798935 DOI: 10.1007/s10458-023-09634-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/15/2023] [Indexed: 01/25/2024]
Abstract
The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human-machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.
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Affiliation(s)
- Berk Buzcu
- Computer Science, Özyeğin University, Istanbul, Turkey
- University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis), Sierre, Switzerland
| | - Melissa Tessa
- Computer Science, High National School of Computer Science ESI ex-INI, Algiers, Algeria
| | - Igor Tchappi
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Amro Najjar
- Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | - Davide Calvaresi
- University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis), Sierre, Switzerland
| | - Reyhan Aydoğan
- Computer Science, Özyeğin University, Istanbul, Turkey
- Interactive Intelligence, Delft University of Technology, Delft, The Netherlands
- University of Alcala, Alcala de Henares, Spain
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Manzo G, Pannatier Y, Duflot P, Kolh P, Chavez M, Bleret V, Calvaresi D, Jimenez-Del-Toro O, Schumacher M, Calbimonte JP. Breast cancer survival analysis agents for clinical decision support. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107373. [PMID: 36720187 DOI: 10.1016/j.cmpb.2023.107373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 12/31/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study.
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Affiliation(s)
- Gaetano Manzo
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland; National Institutes of Health (NIH), Bethesda, MD, USA.
| | - Yvan Pannatier
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
| | - Patrick Duflot
- CHU of Liege, Department of Information System Management, Belgium
| | - Philippe Kolh
- CHU of Liege, Department of Information System Management, Belgium
| | - Marcela Chavez
- CHU of Liege, Department of Information System Management, Belgium
| | | | - Davide Calvaresi
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
| | | | - Michael Schumacher
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
| | - Jean-Paul Calbimonte
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland; The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
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Xiao L, Greer D. Linked Argumentation Graphs for Multidisciplinary Decision Support. Healthcare (Basel) 2023; 11:healthcare11040585. [PMID: 36833121 PMCID: PMC9956294 DOI: 10.3390/healthcare11040585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 02/06/2023] [Accepted: 02/10/2023] [Indexed: 02/18/2023] Open
Abstract
Multidisciplinary clinical decision-making has become increasingly important for complex diseases, such as cancers, as medicine has become very specialized. Multiagent systems (MASs) provide a suitable framework to support multidisciplinary decisions. In the past years, a number of agent-oriented approaches have been developed on the basis of argumentation models. However, very limited work has focused, thus far, on systematic support for argumentation in communication among multiple agents spanning various decision sites and holding varying beliefs. There is a need for an appropriate argumentation scheme and identification of recurring styles or patterns of multiagent argument linking to enable versatile multidisciplinary decision applications. We propose, in this paper, a method of linked argumentation graphs and three types of patterns corresponding to scenarios of agents changing the minds of others (argumentation) and their own (belief revision): the collaboration pattern, the negotiation pattern, and the persuasion pattern. This approach is demonstrated using a case study of breast cancer and lifelong recommendations, as the survival rates of diagnosed cancer patients are rising and comorbidity is the norm.
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Affiliation(s)
- Liang Xiao
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
- Correspondence:
| | - Des Greer
- School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT7 1NN, UK
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Nißen M, Rüegger D, Stieger M, Flückiger C, Allemand M, V Wangenheim F, Kowatsch T. The Effects of Health Care Chatbot Personas With Different Social Roles on the Client-Chatbot Bond and Usage Intentions: Development of a Design Codebook and Web-Based Study. J Med Internet Res 2022; 24:e32630. [PMID: 35475761 PMCID: PMC9096656 DOI: 10.2196/32630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 01/21/2022] [Accepted: 02/17/2022] [Indexed: 11/28/2022] Open
Abstract
Background The working alliance refers to an important relationship quality between health professionals and clients that robustly links to treatment success. Recent research shows that clients can develop an affective bond with chatbots. However, few research studies have investigated whether this perceived relationship is affected by the social roles of differing closeness a chatbot can impersonate and by allowing users to choose the social role of a chatbot. Objective This study aimed at understanding how the social role of a chatbot can be expressed using a set of interpersonal closeness cues and examining how these social roles affect clients’ experiences and the development of an affective bond with the chatbot, depending on clients’ characteristics (ie, age and gender) and whether they can freely choose a chatbot’s social role. Methods Informed by the social role theory and the social response theory, we developed a design codebook for chatbots with different social roles along an interpersonal closeness continuum. Based on this codebook, we manipulated a fictitious health care chatbot to impersonate one of four distinct social roles common in health care settings—institution, expert, peer, and dialogical self—and examined effects on perceived affective bond and usage intentions in a web-based lab study. The study included a total of 251 participants, whose mean age was 41.15 (SD 13.87) years; 57.0% (143/251) of the participants were female. Participants were either randomly assigned to one of the chatbot conditions (no choice: n=202, 80.5%) or could freely choose to interact with one of these chatbot personas (free choice: n=49, 19.5%). Separate multivariate analyses of variance were performed to analyze differences (1) between the chatbot personas within the no-choice group and (2) between the no-choice and the free-choice groups. Results While the main effect of the chatbot persona on affective bond and usage intentions was insignificant (P=.87), we found differences based on participants’ demographic profiles: main effects for gender (P=.04, ηp2=0.115) and age (P<.001, ηp2=0.192) and a significant interaction effect of persona and age (P=.01, ηp2=0.102). Participants younger than 40 years reported higher scores for affective bond and usage intentions for the interpersonally more distant expert and institution chatbots; participants 40 years or older reported higher outcomes for the closer peer and dialogical-self chatbots. The option to freely choose a persona significantly benefited perceptions of the peer chatbot further (eg, free-choice group affective bond: mean 5.28, SD 0.89; no-choice group affective bond: mean 4.54, SD 1.10; P=.003, ηp2=0.117). Conclusions Manipulating a chatbot’s social role is a possible avenue for health care chatbot designers to tailor clients’ chatbot experiences using user-specific demographic factors and to improve clients’ perceptions and behavioral intentions toward the chatbot. Our results also emphasize the benefits of letting clients freely choose between chatbots.
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Affiliation(s)
- Marcia Nißen
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Dominik Rüegger
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.,Pathmate Technologies AG, Zurich, Switzerland
| | - Mirjam Stieger
- Department of Psychology, Brandeis University, Waltham, MA, United States.,Institute of Communication and Marketing, Lucerne University of Applied Sciences and Arts, Lucerne, Switzerland.,Department of Psychology, University of Zurich, Zurich, Switzerland
| | | | - Mathias Allemand
- Department of Psychology, University of Zurich, Zurich, Switzerland.,University Research Priority Programs, Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland
| | - Florian V Wangenheim
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Tobias Kowatsch
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.,Centre for Digital Health Interventions, Institute of Technology Management, University of St.Gallen, St.Gallen, Switzerland
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Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors. J Med Syst 2021; 45:109. [PMID: 34766229 PMCID: PMC8585846 DOI: 10.1007/s10916-021-01770-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 09/15/2021] [Indexed: 11/12/2022]
Abstract
In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient’s high-risk markers, and support treatment decisions.
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