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PubMed1992

Dimensions of knowledge sharing and reuse.

Many workers in medical informatics are seeking to reuse knowledge in new applications and to share encoded knowledge across software environments. Knowledge reuse involves many dimensions, including the reapplication of lexicons, ontologies, inference syntax, tasks, and problem-solving methods. Principal obstacles to all current work in knowledge sharing involve the difficulties of achieving consensus regarding what knowledge representations mean, of enumerating the context features and background knowledge required to ascribe meaning to a particular knowledge representation, and of describing knowledge independent of specific interpreters or inference engines. Progress in the area of knowledge sharing will necessitate more practical experience with attempts to interchange knowledge as well as better tools for viewing and editing knowledge representations at appropriate levels of abstraction. The PROTEGE-II project is one attempt to provide a knowledge-base authoring environment in which developers can experiment with the reuse of knowledge-level problem-solving methods, task models, and domain ontologies.

Artificial Intelligence↗

PubMed2005

Predictive neural networks for gene expression data analysis.

Gene expression data generated by DNA microarray experiments have provided a vast resource for medical diagnosis and disease understanding. Most prior work in analyzing gene expression data, however, focuses on predictive performance but not so much on deriving human understandable knowledge. This paper presents a systematic approach for learning and extracting rule-based knowledge from gene expression data. A class of predictive self-organizing networks known as Adaptive Resonance Associative Map (ARAM) is used for modelling gene expression data, whose learned knowledge can be transformed into a set of symbolic IF-THEN rules for interpretation. For dimensionality reduction, we illustrate how the system can work with a variety of feature selection methods. Benchmark experiments conducted on two gene expression data sets from acute leukemia and colon tumor patients show that the proposed system consistently produces predictive performance comparable, if not superior, to all previously published results. More importantly, very simple rules can be discovered that have extremely high diagnostic power. The proposed methodology, consisting of dimensionality reduction, predictive modelling, and rule extraction, provides a promising approach to gene expression analysis and disease understanding.

Animals↗

PubMed1999

The in-training examination in internal medicine: resident perceptions and lack of correlation between resident scores and faculty predictions of resident performance.

PURPOSE: We sought to survey residents' perceptions regarding the In-Training Examination in Internal Medicine and to assess the ability of faculty members to evaluate the knowledge base of internal medicine residents. SUBJECTS AND METHODS: Residents were asked about the perceived utility of the In-Training Examination and related self-directed educational activities. Residents predicted their own performance on the examination (into upper, middle, or lower tertile). Faculty predicted housestaffs scores, and residents predicted the scores of interns. RESULTS: Most residents (35/36; 97%) believed that the examination was useful, and 91% modified their study habits or clinical rotation schedule based on its results. Approximately half of the residents accurately predicted into which tertile they would score. Faculty predictions of resident performance on the examination were accurate 49% of the time, and resident predictions of intern scores were accurate 38% of the time. The sensitivity ofa lower-tertile prediction by faculty was 34%, with a specificity of 90%. The sensitivity of a resident prediction of a lower-tertile intern score was 15%, with a specificity of 98%. Both faculty and residents were more likely to overestimate than underestimate examination scores. CONCLUSION: Residents believe that the In-Training Examination is useful and frequently initiate educational interventions based on results. Faculty and residents lack the ability to evaluate accurately the knowledge of trainees that they supervise. In particular, both groups may be unable to identify trainees who are deficient in this element of clinical competence.

Clinical Competence↗

PubMed2002

Bone Browser a decision-aid for the radiological diagnosis of bone tumors.

Bone Browser, a decision-aid for radiological diagnosis of bone tumors, was developed in cooperation with the Radiology Department of the Rambam Medical Center, Haifa. The system offers case specific advice from an expert system (ES), general information on the lesion area and allows for recording and retrieving cases. The ES utilizes both rule-based and probabilistic inferencing methodologies to arrive at a differential diagnosis (DD). The knowledge base was validated on 105 cases with known outcome. Clinical evaluation consisted of 59 new cases whose final diagnosis was not known to the evaluators. The correct diagnosis was included in the system's DD in 85% of the cases, which is comparable to the diagnostic accuracy of senior radiologists (88%). The system proved to be helpful to the expert, diagnosing cases missed by the radiologists and suggesting additional diagnoses not listed by the radiologists, raising their diagnostic capability to 91%.

Adolescent↗

PubMed1998

Knowledge coupling: implementing outcomes measurement through the disciplined and routine control of inputs.

The successful implementation of outcomes measurement is jeopardized by its application within systems that do not control inputs. This systemic flaw within health care can be corrected by placing critical knowledge that is now in the minds of individual practitioners into computer-based knowledge coupling tools that can (1) track and control the inputs to the system in a disciplined and routine manner and (2) process information without error or bias. Outcomes then become a natural by-product of what amounts to a paradigm shift in the conceptualization and implementation of health care.

Artificial Intelligence↗

PubMed1998

Studies of antiretroviral therapy in the Multicenter AIDS Cohort Study.

The Food and Drug Administration has been increasingly reliant on surrogate end points to fast-track the licensing of antiretroviral agents. As a result, critical information, such as long-term adverse events, clinical progression rates, survival, and development of resistance, are not available before licensure. Therefore, phase IV studies conducted in large community- and/or clinic-based cohorts will become essential to fill in the gaps in the clinical knowledge base for many of these drugs, and observational cohort studies of HIV-infected patients have been used to address many of these questions. In addition to studies of antiretroviral impact on adverse events and outcomes, such cohorts can provide valuable data on utilization patterns, physician prescribing patterns, and the important issue of patient compliance with increasingly complicated drug regimens.

AIDS-Related Opportunistic Infections↗

PubMed2006

Public knowledge about causes and treatment for schizophrenia: a representative population study.

Research on public knowledge about schizophrenia has so far examined various closed questions eliciting recognition-based knowledge rather than unprompted knowledge. We aim to explore the unprompted popular knowledge regarding causes and treatment of schizophrenia. In a representative survey conducted in Germany in 2001 (N = 5025), two open questions asked respondents to name possible causes and treatment options for schizophrenia. Answers were noted down verbatim and later grouped into categories. Psychosocial and biological causal explanations were equally predominant. Respondents recommended drug treatment more frequently than psychosocial measures like psychotherapy, and they mentioned a doctor's visit or a hospital stay most frequently as the adequate treatment setting. About 45% of respondents knew nothing about possible causes or treatments of schizophrenia. Hence, whereas those confidently naming causes or treatment options for schizophrenia favored professional medical treatment more than previously found, overall knowledge about schizophrenia has thus far been overestimated.

Attitude to Health↗

PubMed1990

Promoting adherence to low-fat, low-cholesterol diets: review and recommendations.

Evidence that lowering blood cholesterol levels reduces risk of coronary heart disease has prompted widespread recommendations that hyperlipidemic individuals undergo dietary therapy. However, the extent to which people can adopt and maintain diets to lower lipids is unclear. In our article, we review what is currently known regarding adherence to low-fat diets and present an approach to dietary counseling for lowering cholesterol that incorporates elements of behavioral self-management and social learning theory. We discuss specific recommendations for counseling hyperlipidemic patients based on the Dietary Alternatives Study. Recommendations include providing patients with an adequate knowledge base to make dietary changes, using goal setting and self-monitoring to help patients initiate dietary changes, enlisting support from the patient's family, and enhancing self-efficacy to promote long-term dietary maintenance.

Cholesterol, Dietary↗

PubMed1990

Diabetes expert systems: planning for long term use.

Expert systems may play an important role in the future in assisting diabetics to control their disease. However the data available suggest that expert systems are difficult to build and more difficult to maintain over a long period. An examination of the problems in maintaining one expert system, GARVAN-ES1, suggests that the problems arise because experts never report on how they reach a decision, rather they justify why the decision is correct. These justifications vary markedly with the context in which they are required, but in context they are accurate and adequate; the difficulties arise in taking them out of context. It is suggested that expert system building techniques must be able to capture knowledge in context and tools must be available to flexibly change the context in which an expert system knowledge base is examined. Two implementations of such strategies, "ripple down rules" and a "knowledge dictionary", are outlined.

Diabetes Mellitus↗

PubMed1997

[Building up nursing knowledge by means of reflexive practice].

After analyzing the prior questions which determine the nursing field contents, the authors propose "reflective practice" as the methodological and pedagogical philosophy of choice in the formation of nurses. According to the authors, the professor is responsible for developing the curriculum. As such, the professor makes decisions regarding course design, methodology, content development, and student evaluation in terms of aptitute, attitude and knowledge acquisition. Research studies seem to demonstrate that the knowledge which, in the end, aids in comprehending the context taught and which determines the decisions a professor takes, is that knowledge based on a reflective practice, a product of the professor's experiences, background, knowledge and active relationship with nursing practice. The contents in this article were presented as a conference in the Third Seminar of Professors of Nursing for General Practice and Surgery.

Curriculum↗

PubMed2005

Effects of information and machine learning algorithms on word sense disambiguation with small datasets.

Current approaches to word sense disambiguation use (and often combine) various machine learning techniques. Most refer to characteristics of the ambiguity and its surrounding words and are based on thousands of examples. Unfortunately, developing large training sets is burdensome, and in response to this challenge, we investigate the use of symbolic knowledge for small datasets. A naïve Bayes classifier was trained for 15 words with 100 examples for each. Unified Medical Language System (UMLS) semantic types assigned to concepts found in the sentence and relationships between these semantic types form the knowledge base. The most frequent sense of a word served as the baseline. The effect of increasingly accurate symbolic knowledge was evaluated in nine experimental conditions. Performance was measured by accuracy based on 10-fold cross-validation. The best condition used only the semantic types of the words in the sentence. Accuracy was then on average 10% higher than the baseline; however, it varied from 8% deterioration to 29% improvement. To investigate this large variance, we performed several follow-up evaluations, testing additional algorithms (decision tree and neural network), and gold standards (per expert), but the results did not significantly differ. However, we noted a trend that the best disambiguation was found for words that were the least troublesome to the human evaluators. We conclude that neither algorithm nor individual human behavior cause these large differences, but that the structure of the UMLS Metathesaurus (used to represent senses of ambiguous words) contributes to inaccuracies in the gold standard, leading to varied performance of word sense disambiguation techniques.

Algorithms↗

PubMed1995

Integration of a data dictionary and a clinical database in an expert system for acute abdominal pain.

Despite promising results, computer-aided diagnosis in acute abdominal pain is rarely used in the clinic. We therefore developed an expert system for acute abdominal pain to be used in clinical routine. The system is based on a new approach integrating a data dictionary, a clinical database and the knowledge base. A data dictionary editor has been developed (C++, WINDOWS, IBM-compatible PC) and a data dictionary for acute abdominal pain has been built up. The clinical database has been linked to a documentation program providing three modes of data entry. The documentation program has been evaluated extensively by clinicians. The integrated approach clearly separates clinical data from knowledge, but guarantees high consistency of data.

Abdominal Pain↗

PubMed1995

Hybrid systems: a promising solution for better decision support tools.

This paper describes HYCONES II, a tool to enable construction of tightly coupled Hybrid Connectionist Expert Systems to solve classification problems. HYCONES II integrates frames with neural networks in its hybrid knowledge base. Three neural network models (CNM, the Fuzzy ARTMAP, and the Semantic ART - SMART) are available to the knowledge engineer. SMART is a new model, introduced by this research group and based on a combination of two previous models. The validation section compares the performance of these three neural models to solve diagnostic problems in two medical domains. Finally, the present status of this project and its future developments are presented.

Algorithms↗

PubMedDate not supplied

Converting a rule-based expert system into a belief network.

The theory of belief networks offers a relatively new approach for dealing with uncertain information in knowledge-based (expert) systems. In contrast with the heuristic techniques for reasoning with uncertainty employed in many rule-based expert systems, the theory of belief networks is mathematically sound, based on techniques from probability theory. It therefore seems attractive to convert existing rule-based expert systems into belief networks. In this article we discuss the design of a belief network reformulation of the diagnostic rule-based expert system HEPAR. For the purpose of this experiment we have studied several typical pieces of medical knowledge represented in the HEPAR system. It turned out that, due to the differences in the type of knowledge represented and in the formalism used to represent uncertainty, much of the medical knowledge required for building the belief network concerned could not be extracted from HEPAR. As a consequence, significant additional knowledge acquisition was required. However, the objects and attributes defined in the HEPAR system, as well as the conditions in production rules mentioning these objects and attributes, were useful for guiding the selection of the statistical variables for building the belief network. The mapping of objects and attributes in HEPAR to statistical variables is discussed in detail.

Artificial Intelligence↗

PubMed2001

Using goal setting as a strategy for dietary behavior change.

Recent reviews have noted that behavioral theory-based nutrition education programs are more successful at achieving food behavior change than knowledge-based programs and that a clear understanding of the mechanisms of behavior change procedures enable dietetics professionals to more effectively promote change. Successful dietary behavior change programs target 1 or more of the personal, behavioral, or environmental factors that influence the behavior of interest and apply theory-based strategies to influence or change those factors. Goal setting is a strategy that is frequently used to help people change. A 4-step goal-setting process has been identified: recognizing a need for change; establishing a goal; adopting a goal-directed activity and self-monitoring it; and self-rewarding goal attainment. The applications of goal setting in dietary interventions for adults and children are reviewed here. Because interventions using goal setting appear to promote dietary change, dietitians should consider incorporating the goal-setting strategies to enhance the behavior change process in nutrition education programs.

Adult↗

PubMedDate not supplied

Mutual patient-psychiatrist communication and the therapeutic contract.

The increasing complexity and subspecialization of medicine make the patient feel lost in the anonymity and strange complexity of modern hospitals, lead to a misunderstanding of the increasing risks and impact of modern treatments, and restrict personal communication due to the prevalence of a knowledge-oriented approach, to a shortage of time, and to insufficient communication skills. All of this may cause increasing dissatisfaction and complaints of patients. Therefore, it is a future task (1) to develop a concept of medicine and psychiatry well-balanced for science-based knowledge and the value-based art of communication, (2) to change the framework of communication in medical and psychiatric practice, and (3) to improve the training of physicians in communication skills in order to fulfill the therapeutic contract successfully.

Communication↗

PubMed1993

Structural relationships of homologous proteins as a fundamental principle in homology modeling.

Protein structure prediction is based mainly on the modeling of proteins by homology to known structures; this knowledge-based approach is the most promising method to date. Although it is used in the whole area of protein research, no general rules concerning the quality and applicability of concepts and procedures used in homology modeling have been put forward yet. Therefore, the main goal of the present work is to provide tools for the assessment of accuracy of modeling at a given level of sequence homology. A large set of known structures from different conformational and functional classes, but various degrees of homology was selected. Pairwise structure superpositions were performed. Starting with the definition of the structurally conserved regions and determination of topologically correct sequence alignments, we correlated geometrical properties with sequence homology (defined by the 250 PAM Dayhoff Matrix) and identity. It is shown that both the topological differences of the protein backbones and the relative positions of corresponding side chains diverge with decreasing sequence identity. Below 50% identity, the deviation in regions that are structurally not conserved continually increases, thus implying that with decreasing sequence identity modeling has to take into account more and more structurally diverging loop regions that are difficult to predict.

Bacterial Proteins↗

PubMed1994

An object-oriented approach to knowledge representation in a biomedical domain.

An object-oriented approach has been applied to the different stages involved in developing a knowledge base about insulin metabolism. At an early stage the separation of terminological and assertional knowledge was made. The terminological component was developed by medical experts and represented in CORE. An object-oriented knowledge acquisition process was applied to the assertional knowledge. A frame description is proposed which includes features like states and events, inheritance and collaboration. States and events are formalized with qualitative calculus. The terminological knowledge was very useful in the development of the assertional component. It assisted in understanding the problem domain, and in the implementation stage, it assisted in building good inheritance hierarchies.

Algorithms↗