1
|
Domínguez J, Prociuk D, Marović B, Čyras K, Cocarascu O, Ruiz F, Mi E, Mi E, Ramtale C, Rago A, Darzi A, Toni F, Curcin V, Delaney B. ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines. Learn Health Syst 2024; 8:e10391. [PMID: 38633019 PMCID: PMC11019374 DOI: 10.1002/lrh2.10391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 07/29/2023] [Accepted: 08/07/2023] [Indexed: 04/19/2024] Open
Abstract
Introduction Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans. Methods We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists. Results Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise. Conclusion An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further.
Collapse
Affiliation(s)
- Jesús Domínguez
- Department of Population Health SciencesKing's College LondonLondonUK
| | | | | | | | | | - Francis Ruiz
- London School of Hygiene and Tropical MedicineLondonUK
| | - Ella Mi
- University of OxfordOxfordUK
| | - Emma Mi
- University of OxfordOxfordUK
| | | | | | | | | | - Vasa Curcin
- Department of Population Health SciencesKing's College LondonLondonUK
| | | |
Collapse
|
2
|
In Search of Embodied Conversational and Explainable Agents for Health Behaviour Change and Adherence. MULTIMODAL TECHNOLOGIES AND INTERACTION 2021. [DOI: 10.3390/mti5090056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Conversational agents offer promise to provide an alternative to costly and scarce access to human health providers. Particularly in the context of adherence to treatment advice and health behavior change, they can provide an ongoing coaching role to motivate and keep the health consumer on track. Due to the recognized importance of face-to-face communication and establishment of a therapist-patient working alliance as the biggest single predictor of adherence, our review focuses on embodied conversational agents (ECAs) and their use in health and well-being interventions. The article also introduces ECAs who provide explanations of their recommendations, known as explainable agents (XAs), as a way to build trust and enhance the working alliance towards improved behavior change. Of particular promise, is work in which XAs are able to engage in conversation to learn about their user and personalize their recommendations based on their knowledge of the user and then tailor their explanations to the beliefs and goals of the user to increase relevancy and motivation and address possible barriers to increase intention to perform the healthy behavior.
Collapse
|
3
|
Čyras K, Oliveira T, Karamlou A, Toni F. Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines. ARGUMENT & COMPUTATION 2021. [DOI: 10.3233/aac-200523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with interacting guideline recommendations in the context of a given patient by means of computational argumentation. In particular, we advance a structured argumentation formalism ABA+G (short for Assumption-Based Argumentation with Preferences (ABA+) and Goals) for integrating and reasoning with information about recommendations, interactions, patient’s state, preferences and prioritised goals. ABA+G combines assumption-based reasoning with preferences and goal-driven selection among reasoning outcomes. Specifically, we assume defeasible applicability of guideline recommendations with the general goal of patient well-being, resolve interactions (conflicts and otherwise undesirable situations) among recommendations based on the state and preferences of the patient, and employ patient-centered goals to suggest interaction-resolving, goal-importance maximising and preference-adhering recommendations. We use a well-established Transition-based Medical Recommendation model for representing guideline recommendations and identifying interactions thereof, and map the components in question, together with the given patient’s state, prioritised goals, and preferences over actions, to ABA+G for automated reasoning. In this, we follow principles of patient management and establish corresponding theoretical properties as well as illustrate our approach in realistic personalised clinical reasoning scenaria.
Collapse
Affiliation(s)
- Kristijonas Čyras
- Department of Computing, Imperial College London, United Kingdom. E-mail:
| | | | - Amin Karamlou
- Department of Computing, Imperial College London, United Kingdom. E-mails: ,
| | - Francesca Toni
- Department of Computing, Imperial College London, United Kingdom. E-mails: ,
| |
Collapse
|
4
|
Mensa E, Colla D, Dalmasso M, Giustini M, Mamo C, Pitidis A, Radicioni DP. Violence detection explanation via semantic roles embeddings. BMC Med Inform Decis Mak 2020; 20:263. [PMID: 33059690 PMCID: PMC7559980 DOI: 10.1186/s12911-020-01237-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 09/02/2020] [Indexed: 11/22/2022] Open
Abstract
Background Emergency room reports pose specific challenges to natural language processing techniques. In this setting, violence episodes on women, elderly and children are often under-reported. Categorizing textual descriptions as containing violence-related injuries (V) vs. non-violence-related injuries (NV) is thus a relevant task to the ends of devising alerting mechanisms to track (and prevent) violence episodes. Methods We present ViDeS (so dubbed after Violence Detection System), a system to detect episodes of violence from narrative texts in emergency room reports. It employs a deep neural network for categorizing textual ER reports data, and complements such output by making explicit which elements corroborate the interpretation of the record as reporting about violence-related injuries. To these ends we designed a novel hybrid technique for filling semantic frames that employs distributed representations of terms herein, along with syntactic and semantic information. The system has been validated on real data annotated with two sorts of information: about the presence vs. absence of violence-related injuries, and about some semantic roles that can be interpreted as major cues for violent episodes, such as the agent that committed violence, the victim, the body district involved, etc.. The employed dataset contains over 150K records annotated with class (V,NV) information, and 200 records with finer-grained information on the aforementioned semantic roles. Results We used data coming from an Italian branch of the EU-Injury Database (EU-IDB) project, compiled by hospital staff. Categorization figures approach full precision and recall for negative cases and.97 precision and.94 recall on positive cases. As regards as the recognition of semantic roles, we recorded an accuracy varying from.28 to.90 according to the semantic roles involved. Moreover, the system allowed unveiling annotation errors committed by hospital staff. Conclusions Explaining systems’ results, so to make their output more comprehensible and convincing, is today necessary for AI systems. Our proposal is to combine distributed and symbolic (frame-like) representations as a possible answer to such pressing request for interpretability. Although presently focused on the medical domain, the proposed methodology is general and, in principle, it can be extended to further application areas and categorization tasks.
Collapse
Affiliation(s)
- Enrico Mensa
- Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy
| | - Davide Colla
- Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy
| | - Marco Dalmasso
- Servizio sovrazonale di Epidemiologia dell'ASL TO3 della Regione Piemonte, Via Sabaudia 164, Grugliasco (TO), 10095, Italy
| | - Marco Giustini
- Reparto Epidemiologia ambientale e sociale Dipartimento Ambiente e Salute (DAMSA) Istituto Superiore di Sanità, Viale Regina Elena, 299, Roma, 00161, Italy
| | - Carlo Mamo
- Servizio sovrazonale di Epidemiologia dell'ASL TO3 della Regione Piemonte, Via Sabaudia 164, Grugliasco (TO), 10095, Italy
| | - Alessio Pitidis
- Reparto Epidemiologia ambientale e sociale Dipartimento Ambiente e Salute (DAMSA) Istituto Superiore di Sanità, Viale Regina Elena, 299, Roma, 00161, Italy.,Data Analysis Services, B2C Innovation Inc. - Digital Services, Corso Magenta 69/A, Milan, PO Box 20123, Italy
| | - Daniele P Radicioni
- Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy.
| |
Collapse
|
5
|
Pease A, Aberdein A, Martin U. Explanation in mathematical conversations: an empirical investigation. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2019; 377:20180159. [PMID: 30966975 PMCID: PMC6365846 DOI: 10.1098/rsta.2018.0159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/22/2018] [Indexed: 06/09/2023]
Abstract
Analysis of online mathematics forums can help reveal how explanation is used by mathematicians; we contend that this use of explanation may help to provide an informal conceptualization of simplicity. We extracted six conjectures from recent philosophical work on the occurrence and characteristics of explanation in mathematics. We then tested these conjectures against a corpus derived from online mathematical discussions. To this end, we employed two techniques, one based on indicator terms, the other on a random sample of comments lacking such indicators. Our findings suggest that explanation is widespread in mathematical practice and that it occurs not only in proofs but also in other mathematical contexts. Our work also provides further evidence for the utility of empirical methods in addressing philosophical problems. This article is part of the theme issue 'The notion of 'simple proof' - Hilbert's 24th problem'.
Collapse
Affiliation(s)
- Alison Pease
- School of Computing, University of Dundee, Dundee, UK
| | - Andrew Aberdein
- School of Arts and Communication, Florida Institute of Technology, Melbourne, FL, USA
| | - Ursula Martin
- Mathematical Institute, University of Oxford, Oxford, UK
| |
Collapse
|
6
|
Nikolaidis S, Kwon M, Forlizzi J, Srinivasa S. Planning with Verbal Communication for Human-Robot Collaboration. ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION 2018. [DOI: 10.1145/3203305] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Human collaborators coordinate effectively their actions through both verbal and non-verbal communication. We believe that the the same should hold for human-robot teams. We propose a formalism that enables a robot to decide optimally between taking a physical action toward task completion and issuing an utterance to the human teammate. We focus on two types of utterances: verbal commands, where the robot asks the human to take a physical action, and state-conveying actions, where the robot informs the human about its internal state, which captures the information that the robot uses in its decision making. Human subject experiments show that enabling the robot to issue verbal commands is the most effective form of communicating objectives, while retaining user trust in the robot. Communicating information about the robot’s state should be done judiciously, since many participants questioned the truthfulness of the robot statements when the robot did not provide sufficient explanation about its actions.
Collapse
Affiliation(s)
- Stefanos Nikolaidis
- The Paul G. Allen Center for Computer Science 8 Engineering, University of Washington
| | | | - Jodi Forlizzi
- Human Computer Interaction, Carnegie Mellon University
| | - Siddhartha Srinivasa
- The Paul G. Allen Center for Computer Science 8 Engineering, University of Washington
| |
Collapse
|
7
|
Abstract
In 1950, Alan Turing proposed his concept of universal machines, emphasizing their abilities to learn, think, and behave in a human-like manner. Today, the existence of intelligent agents imitating human characteristics is more relevant than ever. They have expanded to numerous aspects of daily life. Yet, while they are often seen as work simplifiers, their interactions usually lack social competence. In particular, they miss what one may call authenticity. In the study presented in this paper, we explore how characteristics of social intelligence may enhance future agent implementations. Interviews and an open question survey with experts from different fields have led to a shared understanding of what it would take to make intelligent virtual agents, in particular messaging agents (i.e., chat bots), more authentic. Results suggest that showcasing a transparent purpose, learning from experience, anthropomorphizing, human-like conversational behavior, and coherence, are guiding characteristics for agent authenticity and should consequently allow for and support a better coexistence of artificial intelligence technology with its respective users.
Collapse
|
8
|
García AJ, Simari GR. Defeasible logic programming: DeLP-servers, contextual queries, and explanations for answers. ARGUMENT & COMPUTATION 2014. [DOI: 10.1080/19462166.2013.869767] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
9
|
|
10
|
|
11
|
Guerini M, Stock O, Zancanaro M, O’Keefe DJ, Mazzotta I, de Rosis† F, Poggi I, Lim MY, Aylett R. Approaches to Verbal Persuasion in Intelligent User Interfaces. COGNITIVE TECHNOLOGIES 2011. [DOI: 10.1007/978-3-642-15184-2_29] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
|
12
|
Bentahar J, Moulin B, Bélanger M. A taxonomy of argumentation models used for knowledge representation. Artif Intell Rev 2010. [DOI: 10.1007/s10462-010-9154-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|