Employing Hybrid Reasoning to Support Clinical Decision-Making Sabbir M. Rashid 1[0000-0002-4162-8334] Rensselaer Polytechnic Institute, Troy, NY, 12180, USA Abstract. Clinical reasoning, involving abstraction, abduction, deduc- tion, and induction, is the primary tool that physicians use when making clinical decisions. To support them, we focus on the creation of an AI system that is able to emulate clinical reasoning. We leverage Semantic Web technologies to perform a set of AI tasks involving the various forms of inference associated with clinical reasoning strategies. In particular, for the scope of this work, we focus on clinical problems that require dif- ferential diagnosis techniques. For a given clinical scenario, overlapping reasoning types and strategies may be employed by a physician in con- junction, signifying the need for our AI system to perform hybrid reason- ing. Therefore, we consider the construction of a hybrid reasoner that is compatible with description logics. For medical scenarios where descrip- tion logics may not have some needed expressivity, we consider possible extensions that will allow for the representation of such a scenario. The reasoning system, clinical rule representation, and the resulting recom- mendations will be evaluated based on domain expert consultation in order to determine whether the recommendation aligns with what the expert would recommend. Keywords: Hybrid Reasoning · Deduction · Abduction · Temporal Rea- soning · Knowledge Representation · Diabetes · Ontology · Explainable AI · Inference · Disease · Informatics · Differential Diagnosis · Clinical Reasoning Strategies · Rule Representation 1 Problem statement This thesis focuses on the design and implementation of a clinical decision sup- port system that is able to perform hybrid reasoning by emulating how physi- cians reason. Clinical reasoning is employed in many medical tasks, such as those involving information comprehension, decision-making, and medical error iden- tification. Approaches for clinical reasoning use four distinct types of inference: abstraction, abduction, deduction, and induction [1, 15]. We base our approach on the Select and Test Model (ST-Model), an epistemological framework for medical reasoning in which an expert chooses a plausible hypothesis which is subsequently confirmed or falsified through testing [19]. The cyclic nature of the ST-Model demonstrates how the different types of reasoning can be applied in conjunction during a clinical reasoning framework. Given a set of initial patient