Balancing prescriptions with constraint solvers

Juliana Kuster Filipe Bowles; Marco Bright Caminati

doi:10.1007/978-3-030-17297-8_9

Balancing prescriptions with constraint solvers

Juliana Kuster Filipe Bowles, Marco Bright Caminati

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Clinical guidelines are evidence-based care plans which detail the essential steps to be followed when caring for patients with a specific clinical problem, usually a chronic disease (e.g. diabetes, cardiovascular disease, chronic kidney disease, cancer, chronic obstructive pulmonary disease, and so on). Recommendations for chronic diseases include the medications (or group of medications) to be given at different stages of the treatment plan. We present an automated approach which combines constraint solvers and theorem provers to find the best solutions for treatment according to different criteria, and avoiding adverse drug reactions as much as possible. We extended the approach here to further refine the choice(s) to avoid dangerous or undesirable side effects.

Original language	English
Title of host publication	Automated Reasoning for Systems Biology and Medicine
Editors	Pietro Liò, Paolo Zuliani
Place of Publication	Cham
Publisher	Springer
Pages	243-267
Number of pages	25
ISBN (Electronic)	9783030172978
ISBN (Print)	9783030172961
DOIs	https://doi.org/10.1007/978-3-030-17297-8_9
Publication status	Published - 2019

Publication series

Name	Computational Biology
Publisher	Springer
Volume	30
ISSN (Print)	1568-2684

UN SDGs

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

Access to Document

10.1007/978-3-030-17297-8_9

bookchapter-BC
© Springer Nature Switzerland AG 2019. This work has been made available online in accordance with publisher policies or with permission from the rights holder. Permissions for further reuse of this content should be sought from the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The published version should be used for citation purposes. The final published version of this work is available at https://doi.org/10.1007/978-3-030-17297-8_9
Accepted author manuscript, 800 KB

Rutherford Fellowship: Stochastic models to enable tailoring of medications to patients with multiple morbidities
Kuster Filipe Bowles, J. (PI) & Caminati, M. B. (CoI)
15/02/18 → 13/03/22
Project: Fellowship
Automated Conflict Resolution: Automated Conflic Resolution in Clinical Pathways
Kuster Filipe Bowles, J. (PI)
EPSRC
1/07/15 → 15/12/18
Project: Standard

Juliana Kuster Filipe Bowles
- jkfbst-andrews.acuk
- School of Computer Science - Professor
Person: Academic

Cite this

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title = "Balancing prescriptions with constraint solvers",

abstract = "Clinical guidelines are evidence-based care plans which detail the essential steps to be followed when caring for patients with a specific clinical problem, usually a chronic disease (e.g. diabetes, cardiovascular disease, chronic kidney disease, cancer, chronic obstructive pulmonary disease, and so on). Recommendations for chronic diseases include the medications (or group of medications) to be given at different stages of the treatment plan. We present an automated approach which combines constraint solvers and theorem provers to find the best solutions for treatment according to different criteria, and avoiding adverse drug reactions as much as possible. We extended the approach here to further refine the choice(s) to avoid dangerous or undesirable side effects.",

author = "Bowles, {Juliana Kuster Filipe} and Caminati, {Marco Bright}",

note = "Funding: UK EPSRC grant EP/M014290/1 and MRC grant MR/S003819/1.",

year = "2019",

doi = "10.1007/978-3-030-17297-8_9",

language = "English",

isbn = "9783030172961",

series = "Computational Biology",

publisher = "Springer",

pages = "243--267",

editor = "Pietro Li{\`o} and Zuliani, {Paolo }",

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address = "Netherlands",

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T1 - Balancing prescriptions with constraint solvers

AU - Bowles, Juliana Kuster Filipe

AU - Caminati, Marco Bright

N1 - Funding: UK EPSRC grant EP/M014290/1 and MRC grant MR/S003819/1.

PY - 2019

Y1 - 2019

N2 - Clinical guidelines are evidence-based care plans which detail the essential steps to be followed when caring for patients with a specific clinical problem, usually a chronic disease (e.g. diabetes, cardiovascular disease, chronic kidney disease, cancer, chronic obstructive pulmonary disease, and so on). Recommendations for chronic diseases include the medications (or group of medications) to be given at different stages of the treatment plan. We present an automated approach which combines constraint solvers and theorem provers to find the best solutions for treatment according to different criteria, and avoiding adverse drug reactions as much as possible. We extended the approach here to further refine the choice(s) to avoid dangerous or undesirable side effects.

AB - Clinical guidelines are evidence-based care plans which detail the essential steps to be followed when caring for patients with a specific clinical problem, usually a chronic disease (e.g. diabetes, cardiovascular disease, chronic kidney disease, cancer, chronic obstructive pulmonary disease, and so on). Recommendations for chronic diseases include the medications (or group of medications) to be given at different stages of the treatment plan. We present an automated approach which combines constraint solvers and theorem provers to find the best solutions for treatment according to different criteria, and avoiding adverse drug reactions as much as possible. We extended the approach here to further refine the choice(s) to avoid dangerous or undesirable side effects.

U2 - 10.1007/978-3-030-17297-8_9

DO - 10.1007/978-3-030-17297-8_9

M3 - Chapter

SN - 9783030172961

T3 - Computational Biology

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EP - 267

BT - Automated Reasoning for Systems Biology and Medicine

A2 - Liò, Pietro

A2 - Zuliani, Paolo

PB - Springer

CY - Cham

ER -

Balancing prescriptions with constraint solvers

Abstract

Publication series

UN SDGs

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Projects

Rutherford Fellowship: Stochastic models to enable tailoring of medications to patients with multiple morbidities

Automated Conflict Resolution: Automated Conflic Resolution in Clinical Pathways

Profiles

Juliana Kuster Filipe Bowles

Cite this