A Verified, Executable Formalism for Resilient and Pervasive Guideline-Based Decision Support for Patients

Nick L.S. Fung; Marten J. van Sinderen; Valerie M. Jones; Hermie J. Hermens

doi:10.1007/978-3-030-59137-3_38

A Verified, Executable Formalism for Resilient and Pervasive Guideline-Based Decision Support for Patients

Nick L.S. Fung^*, Marten J. van Sinderen, Valerie M. Jones, Hermie J. Hermens

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

We present an executable formalism for clinical practice guidelines, with the aim of providing pervasive and evidence-based decision support to patients. Unlike traditional formalisms that capture the control flow between tasks, we focus on data flow, with tasks modeled as processes that execute in parallel. By parallelizing and distributing guideline knowledge, each device that constitutes the patient’s pervasive healthcare system can provide decision support independently, avoiding single points of failure. This distribution also enables dynamic system re-configurations, increasing its resilience against evolving requirements and changing communications environments. Our model recognizes four types of processes: Monitoring, Analysis, Decision and Effectuation. These processes were specified using (axiomatic) set theory and implemented as a set of libraries on top of Rosette, which supports execution of the formalism and verification of it using constraint solvers. The formalism was also tested by formalizing a complete clinical guideline for diabetes management, which yielded a Rosette program that was then tested on simulated patient data. The major point of clinical relevance is enhancing the quality and safety of decision support delivered to patients.

Original language	English
Title of host publication	Artificial Intelligence in Medicine
Subtitle of host publication	18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings
Editors	Martin Michalowski, Robert Moskovitch
Publisher	Springer
Pages	427-439
Number of pages	13
ISBN (Electronic)	978-3-030-59137-3
ISBN (Print)	978-3-030-59136-6
DOIs	https://doi.org/10.1007/978-3-030-59137-3_38
Publication status	Published - 26 Sept 2020
Event	18th International Conference on Artificial Intelligence in Medicine, AIME 2020 - Virtual Conference, United States Duration: 25 Aug 2020 → 28 Aug 2020 Conference number: 18

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12299 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Artificial Intelligence in Medicine, AIME 2020
Abbreviated title	AIME 2020
Country/Territory	United States
City	Virtual Conference
Period	25/08/20 → 28/08/20

Keywords

Computerized clinical practice guidelines
Data flow modeling
Diabetes management
Formal specification
Knowledge representation
Pervasive healthcare
Verification and validation
22/2 OA procedure

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-59137-3_38

Fung-2020-A-verified-executable-formalism-forFinal published version, 360 KBLicence: Taverne

Cite this

Fung, N. L. S., van Sinderen, M. J., Jones, V. M., & Hermens, H. J. (2020). A Verified, Executable Formalism for Resilient and Pervasive Guideline-Based Decision Support for Patients. In M. Michalowski, & R. Moskovitch (Eds.), Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings (pp. 427-439). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12299 LNAI). Springer. https://doi.org/10.1007/978-3-030-59137-3_38

Fung, Nick L.S. ; van Sinderen, Marten J. ; Jones, Valerie M. et al. / A Verified, Executable Formalism for Resilient and Pervasive Guideline-Based Decision Support for Patients. Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings. editor / Martin Michalowski ; Robert Moskovitch. Springer, 2020. pp. 427-439 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{714811cd6bf64d5ebef2018199961750,

title = "A Verified, Executable Formalism for Resilient and Pervasive Guideline-Based Decision Support for Patients",

abstract = "We present an executable formalism for clinical practice guidelines, with the aim of providing pervasive and evidence-based decision support to patients. Unlike traditional formalisms that capture the control flow between tasks, we focus on data flow, with tasks modeled as processes that execute in parallel. By parallelizing and distributing guideline knowledge, each device that constitutes the patient{\textquoteright}s pervasive healthcare system can provide decision support independently, avoiding single points of failure. This distribution also enables dynamic system re-configurations, increasing its resilience against evolving requirements and changing communications environments. Our model recognizes four types of processes: Monitoring, Analysis, Decision and Effectuation. These processes were specified using (axiomatic) set theory and implemented as a set of libraries on top of Rosette, which supports execution of the formalism and verification of it using constraint solvers. The formalism was also tested by formalizing a complete clinical guideline for diabetes management, which yielded a Rosette program that was then tested on simulated patient data. The major point of clinical relevance is enhancing the quality and safety of decision support delivered to patients.",

keywords = "Computerized clinical practice guidelines, Data flow modeling, Diabetes management, Formal specification, Knowledge representation, Pervasive healthcare, Verification and validation, 22/2 OA procedure",

author = "Fung, {Nick L.S.} and {van Sinderen}, {Marten J.} and Jones, {Valerie M.} and Hermens, {Hermie J.}",

year = "2020",

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Fung, NLS, van Sinderen, MJ, Jones, VM & Hermens, HJ 2020, A Verified, Executable Formalism for Resilient and Pervasive Guideline-Based Decision Support for Patients. in M Michalowski & R Moskovitch (eds), Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12299 LNAI, Springer, pp. 427-439, 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Virtual Conference, United States, 25/08/20. https://doi.org/10.1007/978-3-030-59137-3_38

A Verified, Executable Formalism for Resilient and Pervasive Guideline-Based Decision Support for Patients. / Fung, Nick L.S.; van Sinderen, Marten J.; Jones, Valerie M. et al.
Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings. ed. / Martin Michalowski; Robert Moskovitch. Springer, 2020. p. 427-439 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12299 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Jones, Valerie M.

AU - Hermens, Hermie J.

N1 - Conference code: 18

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N2 - We present an executable formalism for clinical practice guidelines, with the aim of providing pervasive and evidence-based decision support to patients. Unlike traditional formalisms that capture the control flow between tasks, we focus on data flow, with tasks modeled as processes that execute in parallel. By parallelizing and distributing guideline knowledge, each device that constitutes the patient’s pervasive healthcare system can provide decision support independently, avoiding single points of failure. This distribution also enables dynamic system re-configurations, increasing its resilience against evolving requirements and changing communications environments. Our model recognizes four types of processes: Monitoring, Analysis, Decision and Effectuation. These processes were specified using (axiomatic) set theory and implemented as a set of libraries on top of Rosette, which supports execution of the formalism and verification of it using constraint solvers. The formalism was also tested by formalizing a complete clinical guideline for diabetes management, which yielded a Rosette program that was then tested on simulated patient data. The major point of clinical relevance is enhancing the quality and safety of decision support delivered to patients.

AB - We present an executable formalism for clinical practice guidelines, with the aim of providing pervasive and evidence-based decision support to patients. Unlike traditional formalisms that capture the control flow between tasks, we focus on data flow, with tasks modeled as processes that execute in parallel. By parallelizing and distributing guideline knowledge, each device that constitutes the patient’s pervasive healthcare system can provide decision support independently, avoiding single points of failure. This distribution also enables dynamic system re-configurations, increasing its resilience against evolving requirements and changing communications environments. Our model recognizes four types of processes: Monitoring, Analysis, Decision and Effectuation. These processes were specified using (axiomatic) set theory and implemented as a set of libraries on top of Rosette, which supports execution of the formalism and verification of it using constraint solvers. The formalism was also tested by formalizing a complete clinical guideline for diabetes management, which yielded a Rosette program that was then tested on simulated patient data. The major point of clinical relevance is enhancing the quality and safety of decision support delivered to patients.

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KW - Knowledge representation

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KW - Verification and validation

KW - 22/2 OA procedure

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PB - Springer

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ER -

Fung NLS, van Sinderen MJ, Jones VM, Hermens HJ. A Verified, Executable Formalism for Resilient and Pervasive Guideline-Based Decision Support for Patients. In Michalowski M, Moskovitch R, editors, Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings. Springer. 2020. p. 427-439. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59137-3_38

A Verified, Executable Formalism for Resilient and Pervasive Guideline-Based Decision Support for Patients

Abstract

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Keywords

UN SDGs

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