ArticleJCO clinical cancer informatics2019
Transformation of the National Breast Cancer Guideline Into Data-Driven Clinical Decision Trees.
Article in JCO clinical cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Data-driven decision making in patient management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review.Journal of healthcare engineering · 2023Pooled it
- A Computational Method for Systematic Comparison of Clinical Practice Guidelines.Clinical and public health guidelines · 2026Article
- Informed clinical decisions by outfoxing human FOXN1 variants.The Journal of allergy and clinical immunology · 2025Review
- Leveraging real-world data for continuous evaluation of computational clinical practice guidelines.BMJ health & care informatics · 2025Article
- High-precision information retrieval for rapid clinical guideline updates.NPJ digital medicine · 2025Article
- The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review.Journal of medical systems · 2023Article
- Agreement and consistency in the triaging of musculoskeletal primary care referrals by vetting clinicians using a knowledge-based triage tool.Primary health care research & development · 2023Article
- A novel method for continuous measurements of clinical practice guideline adherence.Learning health systems · 2023Article
- Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer.Asian Pacific journal of cancer prevention : APJCP · 2022Article
- Using guideline-based clinical decision support in oncological multidisciplinary team meetings: A prospective, multicenter concordance study.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2022Observational
- Conversion of a colorectal cancer guideline into clinical decision trees with assessment of validity.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2021Article
- Clinical decision trees support systematic evaluation of multidisciplinary team recommendations.Breast cancer research and treatment · 2020Article
- Pragmatic Considerations on Clinical Decision Support from the 2019 Literature.Yearbook of medical informatics · 2020Review
- Impact on Quality of Documentation and Workload of the Introduction of a National Information Standard for Tumor Board Reporting.JCO clinical cancer informatics · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe essence of guideline recommendations often is intertwined in large texts. This impedes clinical implementation and evaluation and delays timely modular revisions needed to deal with an ever-growing amount of knowledge and application of personalized medicine. The aim of this project was to model guideline recommendations as data-driven clinical decision trees (CDTs) that are clinically interpretable and suitable for implementation in decision support systems.
methodsAll recommendations of the Dutch national breast cancer guideline for nonmetastatic breast cancer were translated into CDTs. CDTs were constructed by nodes, branches, and leaves that represent data items (patient and tumor characteristics [eg, T stage]), data item values (eg, T2 or less), and recommendations (eg, chemotherapy), respectively. For all data items, source of origin was identified (eg, pathology), and where applicable, data item values were defined on the basis of existing classification and coding systems (eg, TNM, Breast Imaging Reporting and Data System, Systematized Nomenclature of Medicine). All unique routes through all CDTs were counted to measure the degree of data-based personalization of recommendations.
resultsIn total, 60 CDTs were necessary to cover the whole guideline and were driven by 114 data items. Data items originated from pathology (49%), radiology (27%), clinical (12%), and multidisciplinary team (12%) reports. Of all data items, 101 (89%) could be classified by existing classification and coding systems. All 60 CDTs could be integrated in an interactive decision support app that contained 376 unique patient subpopulations.
conclusionBy defining data items unambiguously and unequivocally and coding them to an international coding system, it was possible to present a complex guideline as systematically constructed modular data-driven CDTs that are clinically interpretable and accessible in a decision support app.
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