Dialogue Games for Explaining Medication Choices

Shaheen, Qurat-ul-ain; Toniolo, Alice; Bowles, Juliana

doi:10.1007/978-3-030-57977-7_7

Cited by 4 publications

(19 citation statements)

References 17 publications

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“…Transitioning into critical domains, Shaheen et al [14] focused on healthcare, proposing treatment plans for multi-condition patients. Their proposed solution involves employing a Satisfiability Modulo Theories (SMT) Solver [55] to recommend optimal treatment paths, coupled with explainable dialogues to explain the suggestions.…”

Section: Lay Users Use Casesmentioning

confidence: 99%

“…• Dialogue Goal -Is the goal of the dialogue specified? [10,14] -Is there a criterion for when such a goal has been reached, i.e., when the explanation was successful? [10] Understanding explanations about AI systems as a natural communication between humans and machines raises a key theoretical challenge: defining what exactly constitutes a dialogue in this context?…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…• Commencement-and termination-rules: determine how a dialogue game can start or end. [14] Explanation as Argumentation SMT Solver Medicine…”

Section: Dialogue Games and Protocolsmentioning

confidence: 99%

“…For example, while Sklar and Azhar [42] and Arnold et al [46] focus on robotics, Hernandez-Bocanegra and Ziegler [51] turn their attention to hotel recommender systems and Houzé et al [44] to smart home components. Furthermore, Shaheen et al [14] tackle the critical domain of healthcare, explaining the reasoning for treatment strategies for patients grappling with multiple illnesses.…”

Section: Lay Users Use Casesmentioning

confidence: 99%

“…Dialogue systems rooted in argumentation have demonstrated a marked improvement in user comprehension of scientific topics. By engaging users in structured, logical discussions, these systems enhance the depth and clarity of understanding in complex subjects [13,14]. 4.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Beyond One-Shot Explanations: A Systematic Literature Review of Dialogue-Based XAI Approaches

Mindlin,

Beer,

Sieger

et al. 2024

Preprint

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In the last decade, there has been increasing interest in allowing users to understand how the predictions of machine-learned models come about, thus increasing transparency and empowering users to understand and potentially contest those decisions.Dialogue-based approaches, in contrast to traditional one-shot eXplainable Artificial Intelligence (XAI) methods, facilitate interactive, in-depth exploration through multi-turn dialogues, simulating expert conversations. This paper reviews the current state of dialogue-based XAI, presenting a systematic review of 1,339 publications, narrowed down to 14 based on inclusion criteria. We explore theoretical foundations of the systems, propose key dimensions along which different solutions to dialogue-based XAI differ, and identify key use cases, target audiences, system components, and the types of supported queries and responses. Furthermore, we investigate the current paradigms by which systems are evaluated and highlight their key limitations. Key findings include identifying the main use cases, objectives, and audiences targeted by dialogue-based XAI methods, and summarize the main types of questions and information needs. Beyond discussing avenues for future work, we present a meta-architecture for these systems from existing literature and outlined prevalent theoretical frameworks.

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Section: Lay Users Use Casesmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

“…• Commencement-and termination-rules: determine how a dialogue game can start or end. [14] Explanation as Argumentation SMT Solver Medicine…”

Section: Dialogue Games and Protocolsmentioning

confidence: 99%

Section: Lay Users Use Casesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Beyond One-Shot Explanations: A Systematic Literature Review of Dialogue-Based XAI Approaches

Mindlin,

Beer,

Sieger

et al. 2024

Preprint

View full text Add to dashboard Cite

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Argumentation-Based Explanations of Multimorbidity Treatment Plans

Shaheen

Toniolo

Bowles

2021

PRIMA 2020: Principles and Practice of Multi-Agent Systems

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An Explainable AI Model for ICU Admission Prediction of COVID-19 Patients

Dazea¹,

Stefaneas²

2023

Int. J. Artif. Intell. Tools

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COVID-19 has overwhelmed hospitals all around the world, because of the very high number of patients requiring intubation. Predicting and anticipating which patients will be intubated can help hospitals better allocate their resources. For this, we created an explainable AI model that receives some medical data for each patient and returns a prediction on whether they will be intubated later on, while also explaining the argumentation process that led to that conclusion. This model can help medical professionals better manage the ICU beds and the patients between hospitals and warn them on the symptoms and signs to look for in patients worsening. To obtain the model, we first trained various ML algorithms on a dataset of COVID ICU admissions. Then, using the most accurate of the models, we obtained the sum of the most important rules, using R’s inTrees framework. With the rules obtained, we produced a program in Gorgias written in Prolog. We added some more rules and changed the ordering to come up with a program that requires some patient vitals and blood results as inputs and then returns the prediction along with its reasoning.

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Dialogue Games for Explaining Medication Choices

Cited by 4 publications

References 17 publications

Beyond One-Shot Explanations: A Systematic Literature Review of Dialogue-Based XAI Approaches

Beyond One-Shot Explanations: A Systematic Literature Review of Dialogue-Based XAI Approaches

Argumentation-Based Explanations of Multimorbidity Treatment Plans

An Explainable AI Model for ICU Admission Prediction of COVID-19 Patients

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