Case-based learning of plans and goal states in medical diagnosis

Semi-flipped classroom-based learning interventions in a traditional curriculum of oral medicine: students' perceptions and teaching achievements

BACKGROUND

In recent years, flipped classes have emerged and become popular in college medical education. However, due to the huge medical learning system and the limited pre-class study time of students, it is difficult to implement in all courses. And then we adopted the semi-flipped classes (SFCs) to evaluate its teaching effect. This study analysed three educational methods that can be used in oral medicine courses: online education, offline education, and semi-flipped classes.

METHODS

We used two surveys to evaluate the three educational methods. In the first survey 46 teachers and 238 undergraduates shared their experience of the live-streaming and traditional offline courses offered in the different oral medicine curricula; we used anonymous questionnaires to evaluate their class experience. In the second survey 94 students shared their experience of the semi-flipped and traditional classrooms. Students who attended the SFCs in the experimental group learned about the oral mucosa disease by themselves using an online video course and then participated in offline interaction with teachers. The evaluation of the above educational methods was conducted using the anonymous questionnaires and final exam assessment.

RESULTS

According to the first survey, teachers and students both agreed that the overall teaching experience and learning effectiveness in offline education are superior to those in online education. According to the second survey, students who participated in the SFCs performed better in the final exam than those who participated in the simple offline classes. Additionally, the survey showed that the new teaching method helped students gain more knowledge and positively influenced their clinical practice.

CONCLUSIONS

Compared with the online and offline educational methods, the SFC showed better results in both the questionnaire and final exam assessment. Hence, the effectiveness of medical education can be improved by adopting a teaching mode that combines online and offline teaching methods. Scientific and logical SFCs designs, along with their effective implementation, would eventually make SFCs an important tool for medical education.

A hybrid diagnosis model for determining the types of the liver disease

The symptoms of liver diseases are not apparent in the initial stage, and the condition is usually quite serious when the symptoms are obvious enough. Most studies on liver disease diagnosis focus mainly on identifying the presence of liver disease in a patient. Not many diagnosis models have been developed to move beyond the detection of liver disease. The study accordingly aims to construct an intelligent liver diagnosis model which integrates artificial neural networks, analytic hierarchy process, and case-based reasoning methods to examine if patients suffer from liver disease and to determine the types of the liver disease.

An intelligent model for liver disease diagnosis

OBJECTIVES

Liver disease, the most common disease in Taiwan, is not easily discovered in its initial stage; early diagnosis of this leading cause of mortality is therefore highly important. The design of an effective diagnosis model is therefore an important issue in liver disease treatment. This study accordingly employs classification and regression tree (CART) and case-based reasoning (CBR) techniques to structure an intelligent diagnosis model aiming to provide a comprehensive analytic framework to raise the accuracy of liver disease diagnosis.

METHODS

Based on the advice and assistance of doctors and medical specialists of liver conditions, 510 outpatient visitors using ICD-9 (International Classification of Diseases, 9th Revision) codes at a medical center in Taiwan from 2005 to 2006 were selected as the cases in the data set for liver disease diagnosis. Data on 340 patients was utilized for the development of the model and on 170 patients utilized to perform comparative analysis of the models. This paper accordingly suggests an intelligent model for the diagnosis of liver diseases which integrates CART and CBR. The major steps in applying the model include: (1) adopting CART to diagnose whether a patient suffers from liver disease; (2) for patients diagnosed with liver disease in the first step, employing CBR to diagnose the types of liver diseases.

RESULTS

In the first phase, CART is used to extract rules from health examination data to show whether the patient suffers from liver disease. The results indicate that the CART rate of accuracy is 92.94%. In the second phase, CBR is developed to diagnose the type of liver disease, and the new case triggers the CBR system to retrieve the most similar case from the case base in order to support the treatment of liver disease. The new case is supported by a similarity ratio, and the CBR diagnostic accuracy rate is 90.00%. Actual implementation shows that the intelligent diagnosis model is capable of integrating CART and CBR techniques to examine liver diseases with considerable accuracy. The model can be used as a supporting system in making decisions regarding liver disease diagnosis and treatment. The rules extracted from CART are helpful to physicians in diagnosing liver diseases. CBR can retrieve the most similar case from the case base in order to solve a new liver disease problem and can be of great assistance to physicians in identifying the type of liver disease, reducing diagnostic errors and improving the quality and effectiveness of medical treatment.

An information retrieval system for medical records & documents

The forms of the medical records are different from one institute to another. Moreover, medical records are always stored in free text. Consequently, medical records almost can not be logically analyzed and understood by machines. In this paper, we have applied the information retrieval (IR) technique in the using of medical records. We have implemented an IR system for the users, such as doctors and patients, to query similar or related medical records to support diagnosis and treatment. Knowledge retrieval for reuse is the key idea of this system.

Case-based medical informatics

BACKGROUND

The "applied" nature distinguishes applied sciences from theoretical sciences. To emphasize this distinction, we begin with a general, meta-level overview of the scientific endeavor. We introduce the knowledge spectrum and four interconnected modalities of knowledge. In addition to the traditional differentiation between implicit and explicit knowledge we outline the concepts of general and individual knowledge. We connect general knowledge with the "frame problem," a fundamental issue of artificial intelligence, and individual knowledge with another important paradigm of artificial intelligence, case-based reasoning, a method of individual knowledge processing that aims at solving new problems based on the solutions to similar past problems. We outline the fundamental differences between Medical Informatics and theoretical sciences and propose that Medical Informatics research should advance individual knowledge processing (case-based reasoning) and that natural language processing research is an important step towards this goal that may have ethical implications for patient-centered health medicine.

DISCUSSION

We focus on fundamental aspects of decision-making, which connect human expertise with individual knowledge processing. We continue with a knowledge spectrum perspective on biomedical knowledge and conclude that case-based reasoning is the paradigm that can advance towards personalized healthcare and that can enable the education of patients and providers. We center the discussion on formal methods of knowledge representation around the frame problem. We propose a context-dependent view on the notion of "meaning" and advocate the need for case-based reasoning research and natural language processing. In the context of memory based knowledge processing, pattern recognition, comparison and analogy-making, we conclude that while humans seem to naturally support the case-based reasoning paradigm (memory of past experiences of problem-solving and powerful case matching mechanisms), technical solutions are challenging.Finally, we discuss the major challenges for a technical solution: case record comprehensiveness, organization of information on similarity principles, development of pattern recognition and solving ethical issues.

SUMMARY

Medical Informatics is an applied science that should be committed to advancing patient-centered medicine through individual knowledge processing. Case-based reasoning is the technical solution that enables a continuous individual knowledge processing and could be applied providing that challenges and ethical issues arising are addressed appropriately.

Building a case-based diet recommendation system without a knowledge engineer

We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients have special needs in regards to their medical conditions, cultural backgrounds, or special levels of nutrient requirements for better recovery from diseases or surgery, etc. Existing computer support for this task is insufficient-many diets are not specifically tailored for the client's needs or require substantial time of a dietitian to be manually developed. Our approach is based on case-based reasoning, an artificial intelligence technique that finds increasing entry into industrial practice. Our approach goes beyond the traditional case-based reasoning (CBR) approach by allowing an incremental improvement of the system's competency during routine use of the system. The improvement of the system takes place through a direct expert user-system interaction while the expert is accomplishing their tasks of constructing a diet for a given client. Whenever the system performs unsatisfactorily, the expert will need to modify the system-produced diet 'manually', i.e. by entering the desired modifications into the system. Our implemented system, menu construction using an incremental knowledge acquisition system (MIKAS), asks the expert for simple explanations for each of the manual actions he/she takes and incorporates the explanations automatically into its knowledge base (KB) so that the system will perform these manually conducted actions automatically at the next occasion. We present MIKAS and discuss the results of our case study. While still being a prototype, the senior clinical dietitian involved in our evaluation studies judges the approach to have considerable potential to improve the daily routine of hospital dietitians as well as to improve the average quality of the dietary advice given to patients within the limited available time for dietary consultations. Our approach opens up a new avenue towards building highly specialised CBR systems in a more cost-effective way. Hence, our approach promises to allow a significantly more widespread development and practical deployment of CBR systems in a large variety of application domains including many medical applications.

Multiple representations and multi-modal reasoning in medical diagnostic systems

The paper examines the motivations for developing medical diagnostic systems exploiting multiple representations and multi-modal reasoning. The analysis is carried on by revisiting the architectural choices of the CHECK system (developed in late 1980s) which combined heuristic and causal knowledge. The results in the theory of diagnosis and in model-based reasoning (MBR) obtained in early 1990s are used for providing a formal characterization of the notion of diagnosis and of the reasoning mechanisms used in CHECK. The paper addresses also the problem of replacing heuristic knowledge provided by human experts with operational knowledge automatically derived from the deep model. In particular, the pros and cons of knowledge compilation and of the integration of case-based reasoning (CBR) with MBR are discussed by summarizing the experience gained in developing AID and ADAPtER. The problem of using an explicit representation of time in diagnostic systems is analyzed and recent work on the different characterizations of diagnosis arising when the temporal dimension is considered is reported. Finally, the implications of the results obtained in MBR and in temporal reasoning on the future of medical diagnostic systems are briefly discussed.

Case-based prediction in experimental medical studies

Case-based approaches predict the behaviour of dynamic systems by analysing a given experimental setting in the context of others. To select similar cases and to control adaptation of cases, they employ general knowledge. If that is neither available nor inductively derivable, the knowledge implicit in cases can be utilized for a case-based ranking and adaptation of similar cases. We introduce the system OASES and its application to medical experimental studies to demonstrate this approach.

Metareasoning and meta-level learning in a hybrid knowledge-based architecture

Ahybrid knowledge-based architecture integrates different problem solvers for the same (sub)task through a control unit operating at a meta-level, the metareasoner, which coordinates the use of, and the communication between, the different problem solvers. A problem solver is defined to be an association between a knowledge intensive (sub)task, an inference mechanism and a knowledge domain view operated by the inference mechanism in order to perform the (sub)task. Important issues in a hybrid system are the metareasoning and learning aspects. Metareasoning encompasses the functions performed by the metareasoner, while learning reflects the ability of the system to evolve on the basis of its experiences in problem solving. Learning occurs at different levels, learning at the meta-level and learning at the level of the specific problem solvers. Meta-level learning reflects the ability of the metareasoner to improve the overall performance of the hybrid system by improving the efficiency of meta-level tasks. Meta-level tasks include the initial planning of problem solving strategies and the dynamic adaptation of chosen strategies depending on new events occurring dynamically during problem solving. In this paper we concentrate on metareasoning and meta-level learning in the context of a hybrid architecture. The theoretical arguments presented in the paper are demonstrated in practice through a hybrid knowledge-based prototype system for the domain of breast cancer histopathology.

Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals

A new classification algorithm, called VFI5 (for Voting Feature Intervals), is developed and applied to problem of differential diagnosis of erythemato-squamous diseases. The domain contains records of patients with known diagnosis. Given a training set of such records, the VFI5 classifier learns how to differentiate a new case in the domain. VFI5 represents a concept in the form of feature intervals on each feature dimension separately. classification in the VFI5 algorithm is based on a real-valued voting. Each feature equally participates in the voting process and the class that receives the maximum amount of votes is declared to be the predicted class. The performance of the VFI5 classifier is evaluated empirically in terms of classification accuracy and running time.