Explainability Design Patterns in Clinical Decision Support Systems
Authors: Naiseh, M.
Journal: Lecture Notes in Business Information Processing
Volume: 385 LNBIP
Pages: 613-620
eISSN: 1865-1356
ISBN: 9783030503154
ISSN: 1865-1348
DOI: 10.1007/978-3-030-50316-1_45
Abstract:This paper reports on the ongoing PhD project in the field of explaining the clinical decision support systems (CDSSs) recommendations to medical practitioners. Recently, the explainability research in the medical domain has witnessed a surge of advances with a focus on two main methods: The first focuses on developing models that are explainable and transparent in its nature (e.g. rule-based algorithms). The second investigates the interpretability of the black-box models without looking at the mechanism behind it (e.g. LIME) as a post-hoc explanation. However, overlooking the human-factors and the usability aspect of the explanation introduced new risks following the system recommendations, e.g. over-trust and under-trust. Due to such limitation, there is a growing demand for usable explanations for CDSSs to enable the integration of trust calibration and informed decision-making in these systems by identifying when the recommendation is correct to follow. This research aims to develop explainability design patterns with the aim of calibrating medical practitioners trust in the CDSSs. This paper concludes the PhD methodology and literature around the research problem is also discussed.
https://eprints.bournemouth.ac.uk/34804/
Source: Scopus
Explainability Design Patterns in Clinical Decision Support Systems
Authors: Naiseh, M.
Journal: RESEARCH CHALLENGES IN INFORMATION SCIENCE (RCIS 2020)
Volume: 385
Pages: 613-620
eISSN: 1865-1356
ISBN: 978-3-030-50315-4
ISSN: 1865-1348
DOI: 10.1007/978-3-030-50316-1_45
https://eprints.bournemouth.ac.uk/34804/
Source: Web of Science (Lite)
Explainability Design Patterns in Clinical Decision Support Systems
Authors: Naiseh, M.
Conference: The 14th International Conference on Research Challenges in Information Science
Dates: 23-25 September 2020
Journal: Proceedings - International Conference on Research Challenges in Information Science
ISSN: 2151-1349
DOI: 10.1007/978-3-030-50316-1_45
Abstract:This paper reports on the ongoing PhD project in the field of explaining the clinical decision support systems (CDSSs) recommendations to medical practitioners. Recently, the explainability research in the medical domain has witnessed a surge of advances with a focus on two main methods: The first focuses on developing models that are explainable and transparent in its nature (e.g. rule-based algorithms). The second investigates the interpretability of the black-box models without looking at the mechanism behind it (e.g. LIME) as a post-hoc explanation. However, overlooking the human-factors and the usability aspect of the explanation introduced new risks following the system recommendations, e.g. over-trust and under-trust. Due to such limitation, there is a growing demand for usable explanations for CDSSs to enable the integration of trust calibration and informed decision-making in these systems by identifying when the recommendation is correct to follow. This research aims to develop explainability design patterns with the aim of calibrating medical practitioners trust in the CDSSs. This paper concludes the PhD methodology and literature around the research problem is also discussed.
https://eprints.bournemouth.ac.uk/34804/
Source: Manual
Explainability Design Patterns in Clinical Decision Support Systems
Authors: Naiseh, M.
Conference: The 14th International Conference on Research Challenges in Information Science. Proceedings
Pages: 613-620
ISSN: 1865-1348
Abstract:This paper reports on the ongoing PhD project in the field of explaining the clinical decision support systems (CDSSs) recommendations to medical practitioners. Recently, the explainability research in the medical domain has witnessed a surge of advances with a focus on two main methods: The first focuses on developing models that are explainable and transparent in its nature (e.g. rule-based algorithms). The second investigates the interpretability of the black-box models without looking at the mechanism behind it (e.g. LIME) as a post-hoc explanation. However, overlooking the human-factors and the usability aspect of the explanation introduced new risks following the system recommendations, e.g. over-trust and under-trust. Due to such limitation, there is a growing demand for usable explanations for CDSSs to enable the integration of trust calibration and informed decision-making in these systems by identifying when the recommendation is correct to follow. This research aims to develop explainability design patterns with the aim of calibrating medical practitioners trust in the CDSSs. This paper concludes the PhD methodology and literature around the research problem is also discussed.
https://eprints.bournemouth.ac.uk/34804/
http://www.rcis-conf.com/rcis2020/
Source: BURO EPrints