Analyzing interactions on combining multiple clinical guidelines

Veruska Zamborlini, Marcos Da Silveira, Cedric Pruski, Annette ten Teije, Edwin Geleijn, Marike van der Leeden, Martijn Stuiver, Frank van Harmelen

Abstract

Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends a previously proposed knowledge representation model (TMR) to enhance the detection of interactions and it provides a systematic analysis of relevant interactions in the context of multimorbidity. The approach is evaluated in a case study on rehabilitation of breast cancer patients, developed in collaboration with experts. The results are considered promising to support the experts in this task.

Original languageEnglish
JournalArtificial Intelligence in Medicine
DOIs
StatePublished - 21 Mar 2017

Cite this

Zamborlini, Veruska; Da Silveira, Marcos; Pruski, Cedric; ten Teije, Annette; Geleijn, Edwin; van der Leeden, Marike; Stuiver, Martijn; van Harmelen, Frank / Analyzing interactions on combining multiple clinical guidelines.

In: Artificial Intelligence in Medicine, 21.03.2017.

Research output: Scientific - peer-reviewArticle

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abstract = "Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends a previously proposed knowledge representation model (TMR) to enhance the detection of interactions and it provides a systematic analysis of relevant interactions in the context of multimorbidity. The approach is evaluated in a case study on rehabilitation of breast cancer patients, developed in collaboration with experts. The results are considered promising to support the experts in this task.",
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author = "Veruska Zamborlini and {Da Silveira}, Marcos and Cedric Pruski and {ten Teije}, Annette and Edwin Geleijn and {van der Leeden}, Marike and Martijn Stuiver and {van Harmelen}, Frank",
year = "2017",
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Analyzing interactions on combining multiple clinical guidelines. / Zamborlini, Veruska; Da Silveira, Marcos; Pruski, Cedric; ten Teije, Annette; Geleijn, Edwin; van der Leeden, Marike; Stuiver, Martijn; van Harmelen, Frank.

In: Artificial Intelligence in Medicine, 21.03.2017.

Research output: Scientific - peer-reviewArticle

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T1 - Analyzing interactions on combining multiple clinical guidelines

AU - Zamborlini,Veruska

AU - Da Silveira,Marcos

AU - Pruski,Cedric

AU - ten Teije,Annette

AU - Geleijn,Edwin

AU - van der Leeden,Marike

AU - Stuiver,Martijn

AU - van Harmelen,Frank

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AB - Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends a previously proposed knowledge representation model (TMR) to enhance the detection of interactions and it provides a systematic analysis of relevant interactions in the context of multimorbidity. The approach is evaluated in a case study on rehabilitation of breast cancer patients, developed in collaboration with experts. The results are considered promising to support the experts in this task.

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Zamborlini V, Da Silveira M, Pruski C, ten Teije A, Geleijn E, van der Leeden M et al. Analyzing interactions on combining multiple clinical guidelines. Artificial Intelligence in Medicine. 2017 Mar 21. Available from, DOI: 10.1016/j.artmed.2017.03.012