Medical Knowledge-based Systems Research Articles

Amharic based Knowledge-Based System for Diagnosis and Treatment of Chronic Kidney Disease using Machine Learning

Chronic kidney disease is an important challenge for health systems around the world and consuming a huge proportion of health care finances. Around 85% of the world populations live in developing country of the world, where chronic kidney disease prevention programs are undeveloped. Treatment options for chronic kidney disease are not readily available for most countries in sub-Saharan Africa including Ethiopia. Many rural and urban communities in Ethiopia have extremely limited access to medical advice where medical experts are not readily available. To address such a problem, a medical knowledge-based system can play a significant role. Therefore, the aim of this research was developing a self- learning knowledge based system for diagnosis and treatment of the first three stages of kidney disease that can update the knowledge without the involvement of knowledge engineer. In the development of this system, the following procedures are followed: Knowledge Engineering research design was used to develop the prototype system. Purposive sampling strategies were utilized to choose specialists. The information was acquired by using both structured and unstructured interviews and all knowledge’s are represented by using production rule. The represented production rule was modeled by using decision tree modeling approach. Implementation was employed by using pro-log tools. Testing and evolution was performed through test case and user acceptance methods. Finally, we extensively evaluate the prototype system through visual interactions and test cases. Finally, the results show that our approach is better than the current ones.

Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support

BackgroundCapturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians’ experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.ResultsWe evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead.ConclusionsWe observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.

Enabling the use of hereditary information from pedigree tools in medical knowledge-based systems

The use of family information is a key issue to deal with inheritance illnesses. This kind of information use to come in the form of pedigree files, which contain structured information as tree or graphs, which explains the family relationships. Knowledge-based systems should incorporate the information gathered by pedigree tools to assess medical decision making. In this paper, we propose a method to achieve such a goal, which consists on the definition of new indicators, and methods and rules to compute them from family trees. The method is illustrated with several case studies. We provide information about its implementation and integration on a case-based reasoning tool. The method has been experimentally tested with breast cancer diagnosis data. The results show the feasibility of our methodology.

Fuzzy Relational Method를 이용한 CLINAID의 Knowledge Source 신뢰성 조사

Once the medical knowledge-based system has been developed, it is essential to investigate the knowledge sources of the system because knowledge sources can affect the performance of the system in great deal. This paper presents the method and the results of the reliability test done on the medical knowledge-based system CLINAID. A knowledge source tested is Cardiovascular body system data used in CLINAID. The reliability test will be done by investigating structural relationships revealed by fuzzy relational method between the components of the knowledge sources of individual body systems using syndromes as its main component. These partitions are going to be compared with the syndromes elicited from the medical experts. This paper also reports the outcome of the computations using 7 implication operators performed on Cardiovascular body system data.

Roles for knowledge-based computer systems: Case studies in maternity care

The design of medical knowledge-based computer systems requires effective interdisciplinary communication for the development of a community sharing common goals and a common language for design. Over the past 9 years the Perinatal Research Group, an interdisciplinary team of computer scientists, engineers and clinicians, have developed a prototype knowledge-based computer system to aid clinicians in the care of women in labour. The group were uncertain which approach to adopt to progress this system from a prototype to a useful clinical tool to support decision making. A case study and activity theory analysis, of an existing clinical knowledge-based computer system in routine use, helped to resolve a number of communication and methodological issues that the design team encountered. Sharing of backgrounds and perspectives caused the design team to question previous assumptions and to explore alternative functions and roles for knowledge-based computer systems in maternity care. We are now undertaking a longitudinal case study and activity theory analysis of obstetric teams and women in labour to analyse the relationships between clinicians, patients and technology. This work will inform the development of our knowledge-based computer system to place the patient at the centre of the decision-making process.

Cased-Based Reasoning for medical knowledge-based systems

In this paper we present the results of the MIE/GMDS-2000 Workshop ‘Case-Based Reasoning for Medical Knowledge-based Systems’. While in many domains Cased-Based Reasoning (CBR) has become a successful technique for knowledge-based systems, in the medical field attempts to apply the complete CBR cycle are rather exceptional. Some systems have recently been developed, which on the one hand use only parts of the CBR method, mainly the retrieval, and on the other hand enrich the method by a generalisation step to fill the knowledge gap between the specificity of single cases and general rules. And some systems rely on integrating CBR and other problem solving methodologies. In this paper we discuss the appropriateness of CBR for medical knowledge-based systems, point out problems, limitations and possible ways to cope with them.

The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences of the University of Vienna Medical School

The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences pursues methodological research in and practical development of knowledge-based computer systems to assist in the decision-making processes for all areas of medical application. Vagueness of medical terms, uncertainty in the co-occurrence of medical entities, and incompleteness in medical theories are well-known characteristics of medical knowledge and ought to be considered in practically-used medical knowledge-based systems. We found that fuzzy set theory and fuzzy logic are powerful theories that model the above-mentioned characteristics. Fuzzy set theory and fuzzy logic were applied in the following systems: CADIAG-II and MedFrame/CADIAG-IV, FuzzyARDS, and FuzzyKBWean. CADIAG-II and MedFrame/CADIAG-IV are framework programs for consultation systems to aid in the differential diagnostic process in internal medicine. FuzzyARDS is an intelligent on-line monitoring program of data from patients with acute respiratory distress syndrome (ARDS) at an intensive care unit (ICU). It employs fuzzy trend detection and fuzzy automata. FuzzyKBWean is an open-loop fuzzy control program for optimization and quality control of the ventilation and weaning process of patients after cardiac surgery at the ICU. The above-mentioned computer systems have reached the state of extensive clinical integration and testing at the Vienna General Hospital. The obtained results show the applicability and usefulness of these systems.

A Multidimensional and Multigranular Model of Time for Medical Knowledge-Based Systems

In temporal reasoning there are two interrelated issues; how to model time per se and how to model occurrences. In medical temporal reasoning the need for multiple granularities and multiple conceptual temporal contexts arises in relation to a model of time. Some occurrence can then be expressed with respect to different temporal contexts. This paper presents a multidimensional and multigranular model of time for knowledge-based problem solving, primarily for medical applications. Both the conceptual issues and the design issues underlying the implementation of the proposed model are discussed. The presented model of time has been developed in the context of a time ontology for medical knowledge engineering, whose principal primitives are the time-axis and the time-object. The notion of a time-axis constitutes the primitive for the proposed model of time, while the notion of a time-object aims to integrate time with other essential forms of knowledge, such as structural and causal knowledge, in the expression of different types of occurrences, thus resulting in the integral embodiment of time in such occurrences. The notion of a time-object and the overall ontology of occurrences is given only a cursory mention in this paper. The focus of the paper is the time model. More specifically, the paper presents the notion of a time-axis in the context of the overall time ontology and discusses at length the two classes of time-axes, namely the atomic axes and the spanning axes. The assertion language which has been developed, for the entire ontology, for the expression of axioms (deductive rules and integrity constraints), attribute constraints and propagation methods is presented and illustrated. The implementation of the time model in terms of a layered object-based system is also presented.

The Use of Knowledge-based Systems in Medicine in Developing Countries: A Luxury or a Necessity?

Knowledge-based systems (KBSs) in medicine have received much attention over the past two decades, mainly because of the potential benefits that can be gained from using them. They may facilitate in increasing productivity in a medical environment, support the making of diagnoses and other types of medical decisions, assist in the training of medical professionals, and can even handle some routine tasks in a medical environment. However, some critical problems in this field have also been identified. For example, research indicated that some problems can be solved partially, but not completely, with existing artificial intelligence techniques. Another problem is that many of the existing medical information systems do not support the integration of KBSs in a natural way. Furthermore, the routine use of a medical KBS is complicated by legal issues. These and other problems contribute to what we experience today: a large proportion of the medical KB applications that are developed is never actually used in practice. This justifies questions such as: Should developing countries, having limited infrastructure and research resources, invest in medical KBSs research and development, or should this field be regarded as a luxury that only belongs to developed countries?, and: Can developing countries really benefit from the use of these systems? These questions are discussed in this paper. We highlight the main problems surrounding the development and use of medical KBSs. With the focus on developing countries we discuss potential benefits that could be obtained by investing in these systems and we offer guidelines for focusing research and development of medical KBSs.

Effective domain-dependent reuse in medical knowledge bases

Knowledge reuse is now a critical issue for most developers of medical knowledge-based systems. As a rule, reuse is addressed from an ambitious, knowledge-engineering perspective that focuses on reusable general purpose knowledge modules, concepts, and methods. However, such a general goal fails to take into account the specific aspects of medical practice. From the point of view of the knowledge engineer, whose goal is to capture the specific features and intricacies of a given domain, this approach addresses the wrong level of generality. In this paper, we adopt a more pragmatic viewpoint, introducing the less ambitious goal of “domain-dependent limited reuse” and suggesting effective means of achieving it in practice. In a knowledge representation framework combining objects and production rules, we propose three mechanisms emerging from the combination of object-oriented programming and rule-based programming. We show these mechanisms contribute to achieve limited reuse and to introduce useful limited variations in medical expertise.

Effective Domain-Dependent Reuse in Medical Knowledge Bases

Knowledge reuse is now a critical issue for most developers of medical knowledge-based systems. As a rule, reuse is addressed from an ambitious, knowledge-engineering perspective that focuses on reusable general purpose knowledge modules, concepts, and methods. However, such a general goal fails to take into account the specific aspects of medical practice. From the point of view of the knowledge engineer, whose goal is to capture the specific features and intricacies of a given domain, this approach addresses the wrong level of generality. In this paper, we adopt a more pragmatic viewpoint, introducing the less ambitious goal of "domain-dependent limited reuse" and suggesting effective means of achieving it in practice. In a knowledge representation framework combining objects and production rules, we propose three mechanisms emerging from the combination of object-oriented programming and rule-based programming. We show these mechanisms contribute to achieve limited reuse and to introduce useful limited variations in medical expertise.

Logic engineering in medicine

AbstractThe safety-critical nature of the application of knowledge-based systems to the field of medicine requires the adoption of reliable engineering principles with a solid foundation for their construction. Logical languages with their inherent, precise notions of consistency, soundness and completeness provide such a foundation, thus promoting scrupulous engineering of medical knowledge. Moreover, logic techniques provide a powerful means for getting insight into the structure and meaning of medical knowledge used in medical problem solving. Unfortunately, logic is currently only used on a small scale for building practical medical knowledge-based systems. In this paper, the various approaches proposed in the literature are reviewed, and related to the various types of knowledge and problem solving employed in the medical field. The appropriateness of logic for building medical knowledge-based expert systems is further motivated.

A case study in ontology library construction

The goal of our work is to facilitate the development of medical knowledge-based systems by providing a library of reusable ontologies. The availability of such a library reduces the amount of knowledge acquisition required to create knowledge bases of new applications, and makes it easier to connect a knowledge-based system to existing data bases. This article presents a case study in constructing such a library. The emphasis is on studying the principles that underly the internal structure of the library as well as on the process of constructing and using the library. We envision that, in the future, application ontologies can be constructed by the selection and refinement of generic ontologies and domain ontologies from such a library.

Parallel interval-based reasoning in medical knowledge-based system Clinaid

The paper continues a series of papers and a monograph [33], where we have described the conceptual structures as well as the basic architecture of a knowledge-based systemCunaid.

Evaluation of a knowledge-based decision-support system for ventilator therapy management

Evaluation of knowledge-based systems differs from that of conventional systems in terms of verification and validation techniques. Furthermore, evaluating medical decision-support systems is difficult because the field is thus far comparatively unexplored. This paper presents an evaluation of a medical knowledge-based system called VentEx that supports decision-making in the management of ventilator therapy. Real patient data from 1300 hours of patient care involving 12 patients with 6 diagnoses are used to validate the knowledge base. The results range from 4.5% to 15.6% disagreement between the setting recommendations produced by VentEx and a gold standard, and 22.2% disagreement for recommendations for weaning. A comparison between the standard and two physicians showed that VentEx produced advice of the same quality as the physicians.

Knowledge-Acquisition Tools for Medical Knowledge-Based Systems

Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

Foundations for a methodology for medical KBS development

This paper presents the foundations for a methodology for the construction of medical knowledge based systems (KBS). To date, the lack of a methodology that takes into account the typical demands posed by medical environments has hindered the practical application of knowledge technology in medical settings. Our approach views the development process as comprising two activities: the construction of a knowledge level model and the subsequent translation of the model into a computational model in such a way that the connections between the computational structures and their knowledge level counterparts are maintained. The availability of these connections enables a KBS to communicate with domain experts in knowledge level terminology while it can use efficient reasoning techniques for the actual computations. To support the methodology a number of tools have been developed which are described. The approach is illustrated with a scenario in the area of treatment of acute myeloid leukemia.

GAMES II project: a general architecture for medical knowledge-based systems

GAMES II aims at developing a comprehensive and commercially viable methodology to avoid problems ordinarily occurring in KBS development. GAMES II methodology proposes to design a KBSstarting from an epistemological model of medical reasoning (the Select and Test Model). The design is viewed as a process of adding symbol level information to the epistemological model. The architectural framework provided by GAMES II integrates the use of different formalisms and techniques providing a large set of tools. The user can select the most suitable one for representing a piece of knowledge after a careful analysis of its epistemological characteristics. Special attention is devoted to the tools dealing with knowledge acquisition (both manual and automatic). A panel of practicing physicians are assessing the medical value of such a framework and its related tools by using it in a practical application.

Supporting interoperability between medical knowledge-based systems: Experience from pilot implementations

Interoperability may be defined as the ability of knowledge-based systems to function together in a symbiotic manner. Cooperativity implies interoperability but with the added benefit that the output quality of the cooperative network exceeds the overall performance of the participating sub-systems. A number of candidate architectures to support interoperability and cooperativity between medical knowledge-based systems in laboratory medicine domains are now becoming available. Using rapid prototyping techniques, we have demonstrated the feasibility of one of these approaches by carrying out pilot implementations in two unrelated laboratory medicine domains (an internal consistency checking system for validating patients' results in the laboratory and a system for generating alarms and alerts in high dependency units based on laboratory data). The results of this study are discussed in the context of the available techniques so as to provide a basis for further development of cooperative systems in laboratory medicine.

Inferential knowledge acquisition

This paper describes the approach we are pursuing for modeling inferential processes in knowledge-based systems. It is aimed at overcoming the lack of generality affecting many of the systems described in the literature. This mainly happens since the problem-solving method adopted by those systems is too closely tied to the particular domain problem over which the method itself has been modeled. We also describe a system called M-KAT (Medical Knowledge Acquisition Tool) which is useful in simplifying the process of acquiring inferential knowledge. M-KAT relies on an epistemological model of medical reasoning which represents a generalization of most of the problem-solving methods adopted in medical knowledge-based systems. The metarules formalism has been adopted as a mean for representing inferential knowledge and making its acquisition easier, thus allowing the computational implementation of the epistemological model of medical reasoning.