Augmenting Guideline-based CDSS with Experts’ Knowledge

Larburu, Nekane; Muro, Naiara; Macía, Iván; Sánchez, Eider; Wang, Hui; Winder, John; Boaud, Jacques; Séroussi, Brigitte

doi:10.5220/0006213903700376

Cited by 9 publications

(10 citation statements)

References 8 publications

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“…Most of the tools include clinical and pathological risk factors. However, there is variation in the risk factors included, for example, patient age, tumour size, tumour grade, number of positive nodes, ER status, HER2 status, Ki67 status, imaging data, and among others [28,30,32,41]. The patient cohort that the CDSSs are developed with the need to be representative of the population who will use the CDSS.…”

Section: Discussionmentioning

confidence: 99%

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Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Mazo

Kearns

Mooney

et al. 2020

Cancers

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Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.

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Section: Discussionmentioning

confidence: 99%

“…However, users are required to register/subscribe to use this service. DESIREE [32,41] is a web-based tool for the personalised, collaborative and multidisciplinary management of primary breast cancer by specialised Breast Units. DESIREE considers from diagnosis to treatment and follow-up.…”

Section: Cdsss For Treatment Decisions In Breast Cancermentioning

confidence: 99%

Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Mazo

Kearns

Mooney

et al. 2020

Cancers

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“…(i) an image-based breast and tumour characterization tool, which extracts valuable information for diagnosis, treatment, prognosis and follow up from the heterogenous set of image data [1] (ii) a predictive model after breast conservative therapy and radio-biological model, which provides useful results to the clinician and patient from the very first step before conservative therapy [2] (iii) three different clinical decision support systems that coexist and complement each other to give most personalized and best evidence-based recommendations to the BUs [3]:…”

Section: Imentioning

confidence: 99%

DESIREE DEMO - A Web-Based Software Ecosystem for the Personalized, Collaborative and Multidisciplinary Management of Primary Breast Cancer

Larburu

Muro

Macía

2019

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

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Breast cancer is the most overspread cancer in women worldwide, with around 1.7 million new cases every year. Multidisciplinary clinical teams or committees, usually known as Breast Units (BU), are heterogeneous teams composed by all clinical specialist involved in the care of a breast cancer patient (e.g. oncologist, surgeon, …) that aim to discuss these complex clinical cases from all points of view and in the shortest time to provide best health care. BUs base their decisions on the Clinical Practice Guidelines (CPGs), documents that summarize the latest and best evidence-based medicine. Nevertheless, these documents have some knowledge lacks that make them insufficient when working with complex cases "out of the rule", that may represent the 10-20% of the cases. To cope with this pitfall, DESIREE proposes a unified ecosystem that manages all the relevant information of the patients and provides support to clinicians when making a clinical decision in a personalized, collaborative and multidisciplinary way. It is composed by three main components: (i) an image-based breast and tumor characterization tool, (ii) a predictive model after breast conservative therapy and radio-biological model, and (iii) three different clinical decision support systems (i.e. guideline-based, experience-based and patient similarity based) that coexist and complement each other to give most personalized and best evidence-based recommendations to the BUs. All these are supported by DESIMS (i.e. DESiree Information Management System), a Security and Access Control module and an image system for image and models visualization.

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“…One of the main objectives of the DESIREE project is to provide decision support for the therapeutic decisions made by BUs, including surgery, radiotherapy, and systemic therapies. With the aim to provide a personalized state-of-the-art clinical decision support system to BUs, the project provides different modalities of decision support, including guideline (GL), 24 experience (EX) 25 and CB-DSS. In this paper, we present the proposed CB-DSS.…”

Section: Framework and Workflowmentioning

confidence: 99%

Case-Based Decision Support System for Breast Cancer Management

Sekar¹,

Lamy²,

Larburu³

et al. 2018

IJCIS

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Breast cancer is identified as the most common type of cancer in women worldwide with 1.6 million women around the world diagnosed every year. This prompts many active areas of research in identifying better ways to prevent, detect, and treat breast cancer. DESIREE is a European Union funded project, which aims at developing a webbased software ecosystem for the multidisciplinary management of primary breast cancer. The development of an intelligent clinical decision support system offering various modalities of decision support is one of the key objectives of the project. This paper explores case-based reasoning as a problem solving paradigm and discusses the use of an explicit domain knowledge ontology in the development of a knowledge-intensive case-based decision support system for breast cancer management.Keywords: knowledge intensive case-based reasoning, breast cancer, ontology, case-based decision support system, jColibri.

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Augmenting Guideline-based CDSS with Experts’ Knowledge

Cited by 9 publications

References 8 publications

Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Clinical Decision Support Systems in Breast Cancer: A Systematic Review

DESIREE DEMO - A Web-Based Software Ecosystem for the Personalized, Collaborative and Multidisciplinary Management of Primary Breast Cancer

Case-Based Decision Support System for Breast Cancer Management

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