Appendix: Content Summaries of Best Papers for the Decision Support Section of the
2020 IMIA Yearbook
Hendriks MP, Verbeek XAAM, van Vegchel T, van der Sangen MJC, Strobbe LJA, Merkus
JWS, Zonderland HM, Smorenburg CH, Jager A, Siesling S
Transformation of the National Breast Cancer Guideline into data-driven clinical decision
trees
JCO Clin Cancer Inform 2019 May;3:1-14
Since clinical practice guidelines are still narrative and described in large textual
documents, the aim of this work was to model complex guidelines as data-driven clinical
decision trees (CDTs) that could be still human-interpretable while computer-interpretable
for implementation in decision support systems. The Dutch national breast cancer guidelines
were translated into CDTs. Data items, which characterize the patient and the tumor
and represent decisional criteria, were encoded unambiguously using existing classifications
and coding systems related to breast cancer when feasible. In total, 60 CDTs were
necessary to cover the whole guidelines, driven by 114 data items. Of all data items,
101 (89%) could be coded using existing classification and coding systems. All 60
CDTs represented 376 unique patient subpopulations. Complex guidelines could be transformed
as systematically constructed modular data-driven CDTs that are clinically interpretable
and executable in a decision support application.
Kamišalić A, Riaño D, Kert S, Welzer T, Nemec Zlatolas L
Multi-level medical knowledge formalization to support medical practice for chronic
diseases
Data & Knowledge Engineering 2019; 119:36–57
This research is focused on knowledge representation to support the medical processes
involved in chronic diseases management, which can be viewed as a procedural and sequential
application of knowledge. An intuitive, easy, and effective mechanism for medical
knowledge formalization is proposed through a formalism called extended Timed Transition
Diagram (eTTD). This formalism allows for the consistent representation of three basic
levels of decision making that should be taken into account in the prescription and
adaptation of long-term treatment: therapy strategy, dosage, and intolerances. The
methodology can be manually applied to build eTTDs from clinical practice guidelines.
eTTDs implementation is demonstrated by modeling clinical practice guidelines for
the therapeutic management of arterial hypertension. The obtained models can be used
as a baseline framework for the development of decision support systems involving
medical procedures.
Khalifa M, Magrabi F, Gallego B
Developing a framework for evidence-based grading and assessment of predictive tools
for clinical decision support
BMC Med Inform Decis Mak 2019 Oct 29;19(1):207
Deciding to choose a clinical predictive tool in clinical practice should be guided
by its correctly assessed effectiveness. The objective of this work is to developp
a conceptual and practical framework to Grade and Assess Predictive tools (GRASP)
and provide clinicians with a standardised, evidence-based system to support their
search for and selection of efficient predictive tools. The GRASP framework grades
predictive tools based on published evidence across three dimensions: phase of evaluation,
level of evidence, and direction of evidence. The final grade of the tool is based
on the phase of evaluation that gets the hightest grade, supported by the highest
level of positive or mixed evidence that supports a positive conclusion. This framework
was successfully applied to five predictive tools. GRASP report updates could be a
way to maintain a data base that documents the evidence of predictive tools.
