Towards Explainable AI: Design and Development for Explanation of Machine Learning Predictions for a Patient Readmittance Medical Application

Meacham, Sofia; Isaac, Georgia; Nauck, Detlef; Virginas, Botond

doi:10.1007/978-3-030-22871-2_67

Cited by 14 publications

(8 citation statements)

References 4 publications

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“…Trees and Deep Learning [11,12,13,14,15,16,17,18]. Conversely, other approaches exist in the literature which are naturally explainable [19,20,21], but these works do not really discuss the relevance and the quality of these explanations.…”

Section: Explanation Of Machine-learning Modelsmentioning

confidence: 99%

“…We highlight the work of Lundberg et al [15] that proposes an implementation of the Shapley Values in healthcare where the explanatory method is used to prevent hypoxaemia during surgery, and the work of Meacham et al [25], where explainability is used for analysis of patience re-admittance. In Coma-Puig and Carmona [26], Shapley Values are leveraged in order to validate the correctness of the predictive approach in a utility company.…”

Section: Explanation Of Machine-learning Modelsmentioning

confidence: 99%

See 1 more Smart Citation

An Explainable Decision Support System for Predictive Process Analytics

Galanti¹,

Leoni²,

Monaro³

et al. 2022

Preprint

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Predictive Process Analytics is becoming an essential aid for organizations, providing online operational support of their processes. However, process stakeholders need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way. Otherwise, they will be unlikely to trust the predictive monitoring technology and, hence, adopt it. This paper proposes a predictive analytics framework that is also equipped with explanation capabilities based on the game theory of Shapley Values. The framework has been implemented in the IBM Process Mining suite and commercialized for business users. The framework has been tested on real-life event data to assess the quality of the predictions and the corresponding evaluations. In particular, a user evaluation has been performed in order to understand if the explanations provided by the system were intelligible to process stakeholders.

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Section: Explanation Of Machine-learning Modelsmentioning

confidence: 99%

Section: Explanation Of Machine-learning Modelsmentioning

confidence: 99%

An Explainable Decision Support System for Predictive Process Analytics

Galanti¹,

Leoni²,

Monaro³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A significant amount of work in literature is focused on healthcare applications. We highlight [8], an implementation of the Shapley Values in healthcare, where the explanatory method is used to prevent hypoxaemia during surgery, and [10], where explainability is used for analysis of patience re-admittance.…”

Section: B Explanation Of Machine-learning Modelsmentioning

confidence: 99%

Explainable Predictive Process Monitoring

Galanti

Coma-Puig²,

Leoni

et al. 2020

2020 2nd International Conference on Process Mining (ICPM)

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Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanations can be given in the field of predictive business process monitoring.

show abstract

“…A significant amount of work in literature is focused on healthcare applications. We highlight [9], an implementation of the Shapley Values in healthcare, where the explanatory method is used to prevent hypoxaemia during surgery, and [11], where explainability is used for analysis of patience re-admittance.…”

Section: B Explanation Of Machine-learning Modelsmentioning

confidence: 99%