Learning to reason like a doctor: a theoretical and empirical examination of medical students’ sensemaking in the clinical context

The overall aim of this thesis is to explore how the concept of clinical reasoning is understood in the literature, and its relationship with context, as well as to develop a conceptual learning model of how medical students learn to reason in the clinical environment. The thesis consists of two theoretical studies (I and III) and two empirical studies (II and IV). Studies I and III attempt a theoretical analysis of the concepts of clinical reasoning and context, while Studies II and IV use observations and interviews with 23 medical students analysed using grounded theory to describe how clinical reasoning is learned in the clinical environment. Studies I and III identified three conceptualisations of clinical reasoning: “Reasoning as cognitive activity”, characterised as internal cognitive processes, where context is relevant inasmuch as it acts as a specific stimulus for the individual mental processes; “Reasoning as contextually situated activity”, characterised as an emergent process of interaction between the mind and the external world, where context assumes the quality of a co-determinant of clinical reasoning; and “Reasoning as socially mediated activity”, characterised as the social practice of assigning meaning to the patient's illness experience, emphasizing its narrative, linguistic and interpretative nature, where context assumes the quality of the embeddedness of the mind in a world of meaning and experience. Study II developed a conceptual learning model of clinical reasoning. At the core of this model, which attends to the contextually-situated nature of reasoning, are the tensions experienced by the medical students, when elements in a clinical patient encounter challenge their implicit ways of understanding. Study IV further developed the conceptual model introducing the term of clinical sensemaking, which attends both to the contextual and the interpretational nature of reasoning. At the heart of this model are three interdependent processes, which co-constitute clinical sensemaking. These three procedures seek to answer the questions “what is going on here in this situation?”, “what should I do to find out?”, and “how should I manage this?” This thesis proposes clinical sensemaking as an empirically and theoretically informed conceptual model of clinical reasoning and its learning. Clinical sensemaking shifts the attention from the duality of diagnosis and management, as prime determinants of competent practice, to the problem-orientated activity of transforming a clinical situation, into a tangible clinical problem, necessitating appropriate action

Reasoning and querying bounds on differences with layered preferences

Artificial intelligence largely relies on bounds on differences (BoDs) to model binary constraints regarding different dimensions, such as time, space, costs, and calories. Recently, some approaches have extended the BoDs framework in a fuzzy, \u201cnoncrisp\u201d direction, considering probabilities or preferences. While previous approaches have mainly aimed at providing an optimal solution to the set of constraints, we propose an innovative class of approaches in which constraint propagation algorithms aim at identifying the \u201cspace of solutions\u201d (i.e., the minimal network) with their preferences, and query answering mechanisms are provided to explore the space of solutions as required, for example, in decision support tasks. Aiming at generality, we propose a class of approaches parametrized over user\u2010defined scales of qualitative preferences (e.g., Low, Medium, High, and Very High), utilizing the resume and extension operations to combine preferences, and considering different formalisms to associate preferences with BoDs. We consider both \u201cgeneral\u201d preferences and a form of layered preferences that we call \u201cpyramid\u201d preferences. The properties of the class of approaches are also analyzed. In particular, we show that, when the resume and extension operations are defined such that they constitute a closed semiring, a more efficient constraint propagation algorithm can be used. Finally, we provide a preliminary implementation of the constraint propagation algorithms

Ontology-based knowledge representation and semantic search information retrieval: case study of the underutilized crops domain

The aim of using semantic technologies in domain knowledge modeling is to introduce the semantic meaning of concepts in knowledge bases, such that they are both human-readable as well as machine-understandable. Due to their powerful knowledge representation formalism and associated inference mechanisms, ontology-based approaches have been increasingly adopted to formally represent domain knowledge. The primary objective of this thesis work has been to use semantic technologies in advancing knowledge-sharing of Underutilized crops as a domain and investigate the integration of underlying ontologies developed in OWL (Web Ontology Language) with augmented SWRL (Semantic Web Rule Language) rules for added expressiveness. The work further investigated generating ontologies from existing data sources and proposed the reverse-engineering approach of generating domain specific conceptualization through competency questions posed from possible ontology users and domain experts. For utilization, a semantic search engine (the Onto-CropBase) has been developed to serve as a Web-based access point for the Underutilized crops ontology model. Relevant linked-data in Resource Description Framework Schema (RDFS) were added for comprehensiveness in generating federated queries. While the OWL/SWRL combination offers a highly expressive ontology language for modeling knowledge domains, the combination is found to be lacking supplementary descriptive constructs to model complex real-life scenarios, a necessary requirement for a successful Semantic Web application. To this end, the common logic programming formalisms for extending Description Logic (DL)-based ontologies were explored and the state of the art in SWRL expressiveness extensions determined with a view to extending the SWRL formalism. Subsequently, a novel fuzzy temporal extension to the Semantic Web Rule Language (FT-SWRL), which combines SWRL with fuzzy logic theories based on the valid-time temporal model, has been proposed to allow modeling imprecise temporal expressions in domain ontologies

An introduction to DSmT

The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this introduction, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT), developed for dealing with imprecise, uncertain and conflicting sources of information. We focus our presentation on the foundations of DSmT and on its most important rules of combination, rather than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout this presentation to show the efficiency and the generality of this new approach

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

A hybrid approach to reasoning under uncertainty

A complete approach to reasoning under uncertainty requires support for both identification of the appropriate hypothesis space and ranking hypotheses based on available evidence. We present a hybrid reasoning scheme which combines symbolic and numeric methods for uncertainty management to provide efficient and effective support for both of these tasks. The hybrid is based on symbolic techniques adapted from Assumption-based Truth Maintenance systems (ATMS), combined with numeric methods adapted -from the Dempster/Shafer theory of evidence, as extended in Bald­win's Support Logic Programming system. The hybridization is achieved by viewing an ATMS as a symbolic algebra system for uncertainty calculations, This technique has several major advantages over conventional methods for performing inference with numeric certainty estimates in addition to the ability to dynamically determine hypothesis spaces, including improved management of dependent and partially in­dependent evidence, faster run-time evaluation of propositional certainties1 and the ability to query the certainty value of a proposition from multiple perspectives