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Project name: TiMeDDx Multi–Phase Anchor-Based Diagnostic Decision-Support Method and System 

Principal researcher name: Mor Peleg and Yaron Denekamp


Invention area: Healthcare IT




Main clinical manifestation (MCM)-oriented diagnosis starts with a chief problem and reasons about possible diagnoses that can be manifested in that way. The reasoning process often starts by considering abstract diagnosis groups (e.g., infectious vs. non-infectious diarrhea) and refines them.Most existing diagnostic decision-support systems (DSSs) are not specially tailored toward MCM-oriented diagnosis. We developed a prototype diagnostic decision-support model called TiMeDDx that is MCM-oriented and follows the hypothetico-deductive clinical reasoning process of differential diagnosis. The model guides users in a phase-by-phase manner regarding abstract diagnosis groups and diagnoses that should be considered and appropriate data that should be collected during the clinical investigation process.TiMeDDx's knowledge base contains, when possible, knowledge derived from MCM-oriented evidence-based sources.




We developed a knowledge model for MCM-oriented diagnosis and two diagnostic algorithms: Bayesian and heuristic. We called the system composed of the knowledge model and algorithms "TiMeDDx". We evaluated our system using the clinical problem of diarrhea and the problem of syncope as case studies.




Our project supports:


(1) Acquiring the clinical knowledge and keeping it up to date. Knowledge can be acquired by eliciting it from domain experts or it can be gathered from the literature or by compiling data found in electronic medical record systems;


(2) Representing and reasoning with the clinical knowledge. The main decision-support models are quantitative (e.g., statistical models including Bayesian networks, machine learning approaches) or qualitative (e.g., heuristic knowledge represented as rules, ontologies, or decision tables);


(3) Supporting the sequence of reasoning used in the problem-oriented diagnosis process;




A decision-support system based on this model can support physicians in making correct evidence-based diagnosis without unnecessary and costly tests and without wasting time