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Objective: This manuscript describes the development of a general intelligent tutoring system for teaching visual classification problem solving., Materials and Methods: The approach is informed by cognitive theory, previous empirical work on expertise in diagnostic problem-solving, and our own prior work describing the development of expertise in pathology. The architecture incorporates aspects of cognitive tutoring system and knowledge-based system design within the framework of the unified problem-solving method description language component model. Based on the domain ontology, domain task ontology and case data, the abstract problem-solving methods of the expert model create a dynamic solution graph. Student interaction with the solution graph is filtered through an instructional layer, which is created by a second set of abstract problem-solving methods and pedagogic ontologies, in response to the current state of the student model., Results: In this paper, we outline the empirically derived requirements and design principles, describe the knowledge representation and dynamic solution graph, detail the functioning of the instructional layer, and demonstrate two implemented interfaces to the system., Conclusion: Using the general visual classification tutor, we have created SlideTutor, a tutoring system for microscopic diagnosis of inflammatory diseases of skin.

This paper describes a problem-solving method for modelling the 'unprotocolised' treatment administration task in medicine. We argue that there are medical domains in which no well-established standard treatment protocols exist, and the physician has to decide on the therapy that is to be applied to each patient, in function of a set of therapeutic objectives to be fulfilled. For this reason, we propose the modelling of this type of task adapting the generic class of problem resolution methods for design task, labelled as Propose-Critique-Modify (PCM). In this paper, we are presenting a model of expertise which has been developed using the basic modelling components of the CommonKADS methodology.

Objective: Using the deep learning model to realize tongue image-based disease location recognition and focus on solving two problems: 1. The ability of the general convolution network to model detailed regional tongue features is weak; 2. Ignoring the group relationship between convolution channels, which caused the high redundancy of the model.Methods: To enhance the convolutional neural networks. In this paper, a stochastic region pooling method is proposed to gain detailed regional features. Also, an inner-imaging channel relationship modeling method is proposed to model multi-region relations on all channels. Moreover, we combine it with the spatial attention mechanism.Results: The tongue image dataset with the clinical disease-location label is established. Abundant experiments are carried out on it. The experimental results show that the proposed method can effectively model the regional details of tongue image and improve the performance of disease location recognition.Conclusion: In this paper, we construct the tongue image dataset with disease-location labels to mine the relationship between tongue images and disease locations. A novel fully-channel regional attention network is proposed to model the local detail tongue features and improve the modeling efficiency.Significance: The applications of deep learning in tongue image disease-location recognition and the proposed innovative models have guiding significance for other assistant diagnostic tasks. The proposed model provides an example of efficient modeling of detailed tongue features, which is of great guiding significance for other auxiliary diagnosis applications. [ABSTRACT FROM AUTHOR]

Objective: Case-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones.Methods: Knowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types.Material: This framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies.Results: The resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems.Conclusion: Building an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems. [ABSTRACT FROM AUTHOR]

Abstract: Objectives: Balancing the trade-offs between solution quality, problem-solving efficiency, and transparency is an important challenge in medical applications of conversational case-based reasoning (CCBR). For example, test selection in CCBR is often based on strategies in which the absence of a specific hypothesis (e.g., diagnosis) to be confirmed makes it difficult to explain the relevance of test results that users are asked to provide. In this paper, we present an approach to CCBR in medical classification and diagnosis that aims to increase transparency while also providing high levels of accuracy and efficiency. Methods: We present an algorithm for CCBR called iNN(k) in which feature selection is driven by the goal of confirming a target class and informed by a measure of a feature''s discriminating power in favor of the target class. As we demonstrate in a CCBR system called CBR-Confirm, this enables a CCBR system to explain the relevance of any question it asks the user. We evaluate the algorithm''s accuracy and efficiency on a selection of datasets related to medicine and health care. Results: The performance of iNN(k) on a given dataset is shown to depend on the value of k and on whether local or global feature selection is used in the algorithm. The combination of these parameters for which iNN(k) is most effective in addressing the trade-off between accuracy and efficiency is identified for each of the selected datasets. For example, only 42% and 51% on average of features in a complete problem description were needed by iNN(k) to provide accuracy levels of 86.5% and 84.3% respectively on the lymphography and SPECT heart datasets from the UCI machine learning repository. Conclusion: Our results demonstrate the ability of iNN(k) to provide high levels of accuracy on most of the selected datasets, while often requiring the user to provide only a small subset of the features in a complete problem description, and enabling a CCBR system to explain the relevance of any question it asks the user. [Copyright &y& Elsevier]

Abstract: Objective: To describe and illustrate a four stage methodological approach to capture user knowledge in a biomedical domain area, use that knowledge to design an ontology, and then implement and evaluate the ontology as a health information system (HIS). Methods and materials: A hybrid participatory design-grounded theory (GT-PD) method was used to obtain data and code them for ontology development. Prototyping was used to implement the ontology as a computer-based tool. Usability testing evaluated the computer-based tool. Results: An empirically derived domain ontology and set of three problem-solving approaches were developed as a formalized model of the concepts and categories from the GT coding. The ontology and problem-solving approaches were used to design and implement a HIS that tested favorably in usability testing. Conclusions: The four stage approach illustrated in this paper is useful for designing and implementing an ontology as the basis for a HIS. The approach extends existing ontology development methodologies by providing an empirical basis for theory incorporated into ontology design. [Copyright &y& Elsevier]