Author, as appears in the article.: Kamisalic, Aida; Riano, David; Kert, Suzana; Welzer, Tatjana; Zlatolas, Lili Nemec

Department: Enginyeria Informàtica i Matemàtiques

URV's Author/s: RIAÑO RAMOS, DAVID

Keywords: Medical procedural knowledge Knowledge representation Decision support systems Decision making

Abstract: Medical processes combine medical actions which are performed by health care professionals while they observe signs and symptoms and decide about interventions, prescriptions, or tests, in order to deal with the health problem that affects a particular patient. Our research was centered in the representation of knowledge for the purpose of decision making in medical processes for chronic diseases. In order to achieve this objective, we followed three steps: (1) We performed an analysis and comparison of formal languages for procedural knowledge representation from a decision-making perspective. (2) We proposed an intuitive, easy, and effective mechanism of medical knowledge formalization. And, (3) we defined a methodology to model medical processes. Our new formalism to represent knowledge is called the extended Timed Transition Diagram (eTTD) and can be used to describe three basic levels of decision making in a long-term treatment: therapy strategy, dosage, and intolerances. The methodology can be applied manually to build eTTDs from clinical practice guidelines (CPGs) or automatically to construct them using the information available in clinical records about individual, multi-level medical processes. We validated eTTDs with clinical practice guidelines for arterial hypertension. The obtained models can be used as a baseline framework for medical, procedural decision support systems development.

Thematic Areas: Interdisciplinar Information systems and management Engenharias iv Engenharias i Computer science, information systems Computer science, artificial intelligence Ciencias sociales Ciência da computação

licence for use: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 0169023X

Author's mail: david.riano@urv.cat

Author identifier: 0000-0002-1608-0215

Record's date: 2022-07-09

Papper version: info:eu-repo/semantics/acceptedVersion

Link to the original source: https://www.sciencedirect.com/science/article/abs/pii/S0169023X16303937?via%3Dihub

Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/

Papper original source: Data & Knowledge Engineering. 119 36-57

APA: Kamisalic, Aida; Riano, David; Kert, Suzana; Welzer, Tatjana; Zlatolas, Lili Nemec (2019). Multi-level medical knowledge formalization to support medical practice for chronic diseases. Data & Knowledge Engineering, 119(), 36-57. DOI: 10.1016/j.datak.2018.12.001

Article's DOI: 10.1016/j.datak.2018.12.001

Entity: Universitat Rovira i Virgili

Journal publication year: 2019

Publication Type: Journal Publications