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PubMed2005

The role of biomedical knowledge in clinical reasoning: a lexical decision study.

PURPOSE: To investigate the role of biomedical and diagnostic inferences in clinical reasoning of advanced medical students and experienced family physicians using a lexical decision task. METHOD: In 2002, 15 family physicians and 20 fourth-year medical students at Maastricht University medical school in The Netherlands were instructed to carefully study 60 short clinical texts consisting of signs and symptoms associated with a particular disease. Participants read the texts on a computer screen and responded using a computer keyboard. Each text was followed by a target item (i.e., biomedical item, diagnostic item, or a nonword). Participants had to decide as quickly and accurately as possible whether the presented target item was a word or a nonword. For both groups, mean response time and mean error rate for all levels of item type were analyzed. RESULTS: Findings indicate that both physicians and medical students judged diagnostic target items faster and more accurately than biomedical target items. However, physicians were considerably faster than were students on judging biomedical and diagnostic target items. CONCLUSIONS: These findings are largely in line with knowledge encapsulation in that biomedical knowledge still plays a prominent role in the physician's clinical reasoning.

Adult↗
PubMed1997

A case-based consiliarius for therapy recommendation (ICONS): computer-based advice for calculated antibiotic therapy in intensive care medicine.

We report here on the system ICONS which utilizes case-based reasoning for medical decision support. As an application domain we have chosen the medical field of 'calculated antibiotic therapy' in an intensive care medicine setting. The system ICONS which runs on a personal computer suggests adequate antibiotic therapy regimen satisfying medical and economic conditions. To speed up the process of finding an adequate antibiotic therapy for a current patient, case-based reasoning is used for finding previously documented similar cases and for modifying them according to the requirements of the current patient. To reduce the memory capacity for the documentation of cases, collections of similar cases are clustered to prototypes. Medical knowledge is represented within a hierarchy of such prototypes and cases and an additional context-sensitive background knowledge-base. A knowledge acquisition tool was programmed that allows revisions of the background medical knowledge-base by simple and comprehensive methods. In addition to the advantage of producing site-specific and time-dependent knowledge, case-based reasoning is a practical method for speeding-up the process of generating and evaluating hypotheses in medical classification tasks.

Anti-Bacterial Agents↗
PubMedDate not supplied

A cooperative approach to decision support in the differential diagnosis of breast disease.

The problem of differential diagnosis in breast histopathology is described and areas in need of decision support identified. It is argued that the conventional role played by decision support systems, i.e. that of the 'autonomous problem-solver', is unacceptable in this domain and that support must be given to the actual cognitive functions of potential system users. Decision-making processes of pathologists are examined with particular reference to Rector's category of 'broad experts'. Pathologists possess a rich set of general diagnostic concepts for their domain, but their knowledge of detailed aspects of differential diagnosis may contain gaps and errors. A cooperative model of decision support, which utilizes the strengths and complements the weaknesses of pathologists' problem-solving skills, is described and the operation of this model within a functioning system illustrated.

Artificial Intelligence↗
PubMed2006

Using data mining to explore complex clinical decisions: A study of hospitalization after a suicide attempt.

BACKGROUND: Medical education is moving toward developing guidelines using the evidence-based approach; however, controlled data are missing for answering complex treatment decisions such as those made during suicide attempts. A new set of statistical techniques called data mining (or machine learning) is being used by different industries to explore complex databases and can be used to explore large clinical databases. METHOD: The study goal was to reanalyze, using data mining techniques, a published study of which variables predicted psychiatrists' decisions to hospitalize in 509 suicide attempters over the age of 18 years who were assessed in the emergency department. Patients were recruited for the study between 1996 and 1998. Traditional multivariate statistics were compared with data mining techniques to determine variables predicting hospitalization. RESULTS: Five analyses done by psychiatric researchers using traditional statistical techniques classified 72% to 88% of patients correctly. The model developed by researchers with no psychiatric knowledge and employing data mining techniques used 5 variables (drug consumption during the attempt, relief that the attempt was not effective, lack of family support, being a housewife, and family history of suicide attempts) and classified 99% of patients correctly (99% sensitivity and 100% specificity). CONCLUSIONS: This reanalysis of a published study fundamentally tries to make the point that these new multivariate techniques, called data mining, can be used to study large clinical databases in psychiatry. Data mining techniques may be used to explore important treatment questions and outcomes in large clinical databases and to help develop guidelines for problems where controlled data are difficult to obtain. New opportunities for good clinical research may be developed by using data mining analyses.

Adult↗
PubMed2002

Leveraging intelligent agents for knowledge discovery from heterogeneous healthcare data repositories.

This paper presents a case for an intelligent agent based framework for knowledge discovery in a distributed healthcare environment comprising multiple heterogeneous healthcare data repositories. Data-mediated knowledge discovery, especially from multiple heterogeneous data resources, is a tedious process and imposes significant operational constraints on end-users. We demonstrate that autonomous, reactive and proactive intelligent agents provide an opportunity to generate end-user oriented, packaged, value-added decision-support/strategic planning services for healthcare professionals, manages and policy makers, without the need for a priori technical knowledge. Since effective healthcare is grounded in good communication, experience sharing, continuous learning and proactive actions, we use intelligent agents to implement an Agent based Data Mining Infostructure that provides a suite of healthcare-oriented decision-support/strategic planning services.

Efficiency, Organizational↗
PubMed1991

Intelligent management of epidemiologic data.

In the lifecycle of epidemiologic data three steps can be identified: production, interpretation and exploitation for decision. Computerized support can be precious, if not indispensable, at any of the three levels, therefore several epidemiologic data management systems were developed. In this paper we focus on intelligent management of epidemiologic data, where intelligence is needed in order to analyze trends or to compare observed with reference value and possibly detect abnormalities. After having outlined the problems involved in such a task, we show the features of ADAMS, a system realized to manage aggregated data and implemented in a personal computer environment.

Artificial Intelligence↗
PubMed1990

Treatment of chronic heart failure: an expert system advisor for general practitioners.

Most decision-support systems in medicine have been developed in hospital environments, but only few are designed for being used by general practitioners. The present work aims to design an expert system for practitioners in chronic heart failure (CHF) treatment. It provides assistance in defining the therapy relying on CHF aetiology, gravity, physiopathological conditions, and discriminates if other coexistent diseases and/or drugs taken by the patient could interact with CHF management. It warns the physician about the possible interactions of the considered CHF therapy. In case of contraindications, the system suggests another alternative therapy. It also advices about the control tests to follow-up the prescribed therapy, and about the indicated hygienic-dietetic suggestions. To assess its internal consistency, we examined the behaviour of the system with 20 CHF patients, by comparing the suggested therapy with the prescriptions of cardiologists. In 9 cases the suggested therapeutic schemes contained all the "n" drugs administered by the cardiologists. In 5 cases the concordance was on at least two thirds of the prescribed medications, in 5 between one half and two thirds, while in 1 case there was no concordance at all. In none of the 10 cases with partial concordance, were there major discrepancies (i.e. potentially deleterious for the patient) between the expert system's suggestions and the cardiologists' prescriptions. In conclusion, the advices of the expert system were similar to those of the cardiologists, suggesting the feasibility of such a computer support to CHF management.

Anti-Arrhythmia Agents↗
PubMed2006

A study of evolutionary multiagent models based on symbiosis.

Multiagent Systems with Symbiotic Learning and Evolution (Masbiole) has been proposed and studied, which is a new methodology of Multiagent Systems (MAS) based on symbiosis in the ecosystem. Masbiole employs a method of symbiotic learning and evolution where agents can learn or evolve according to their symbiotic relations toward others, i.e., considering the benefits/losses of both itself and an opponent. As a result, Masbiole can escape from Nash Equilibria and obtain better performances than conventional MAS where agents consider only their own benefits. This paper focuses on the evolutionary model of Masbiole, and its characteristics are examined especially with an emphasis on the behaviors of agents obtained by symbiotic evolution. In the simulations, two ideas suitable for the effective analysis of such behaviors are introduced; "Match Type Tile-world (MTT)" and "Genetic Network Programming (GNP)". MTT is a virtual model where tile-world is improved so that agents can behave considering their symbiotic relations. GNP is a newly developed evolutionary computation which has the directed graph type gene structure and enables to analyze the decision making mechanism of agents easily. Simulation results show that Masbiole can obtain various kinds of behaviors and better performances than conventional MAS in MTT by evolution.

Algorithms↗
PubMed1989

Understanding the beliefs of Jehovah's Witnesses.

The individual religious belief system of the patient who is one of Jehovah's Witnesses regarding the refusal of blood products may have a direct impact on his or her treatment and its outcome. The critical care nurse is in a unique and powerful position to support this patient through the many potentially stressful clinical and emotional situations that this decision may engender. A thorough understanding of the rationale behind the Jehovah's Witnesses' belief system is necessary to provide intelligent and ethical nursing care.

Blood Transfusion↗
PubMed2002

[Expert systems in gastroenterology].

Expert systems are the software systems developed by the application of the various intelligence, which could successfully compete to peoples-experts, and have the consultant function with the characteristics of the explanations and the advices in some specific domain. These are, in essence, the intelligent information systems, which consists several thousands of the rules from the definite problem field and which are capable to explain their decisions. The knowledge systems are lesser software systems, also developed by means of the technique of the artificial intelligence which are usually less successful in some field of knowledge of the experts. In the paper are presented the basic characteristics three most often applied expert systems in gastroenterology: Quick Medical Reference (QMR), HEPAT, ILIAD.

Diagnosis, Computer-Assisted↗
PubMed2001

[Using projective tests in forensic psychiatry may lead to wrong conclusions. Only empirically tested tests should be used].

The use of projective and psychometric psychological tests at the Department of Forensic Psychiatry in Stockholm (Huddinge), Sweden, was studied for a population of 60 men, including many patients with neuropsychological disabilities and multiple psychiatric disorders. The results showed that the use of projective tests like Rorschach, Object Relations Test, and House-Tree-Person was more frequent than the use of objective psychometric tests. Neuropsychological test batteries like the Halstead-Reitan Neuropsychological Test Battery or Luria-Nebraska Neuropsychological Battery were not used. The majority of patients were, however, assessed by intelligence scales like the WAIS-R. The questionable reliability and validity of the projective tests, and the risk of subjective interpretations, raise a problem when used in a forensic setting, since the courts' decisions about a sentence to prison or psychiatric care is based on the forensic psychiatric assessment. The use of objective psychometric neuropsychological tests and personality tests is recommended.

Decision Making↗
PubMed2001

Computer-automated dementia screening using a touch-tone telephone.

BACKGROUND: This study investigated the sensitivity and specificity of a computer-automated telephone system to evaluate cognitive impairment in elderly callers to identify signs of early dementia. METHODS: The Clinical Dementia Rating Scale was used to assess 155 subjects aged 56 to 93 years (n = 74, 27, 42, and 12, with a Clinical Dementia Rating Scale score of 0, 0.5, 1, and 2, respectively). These subjects performed a battery of tests administered by an interactive voice response system using standard Touch-Tone telephones. Seventy-four collateral informants also completed an interactive voice response version of the Symptoms of Dementia Screener. RESULTS: Sixteen cognitively impaired subjects were unable to complete the telephone call. Performances on 6 of 8 tasks were significantly influenced by Clinical Dementia Rating Scale status. The mean (SD) call length was 12 minutes 27 seconds (2 minutes 32 seconds). A subsample (n = 116) was analyzed using machine-learning methods, producing a scoring algorithm that combined performances across 4 tasks. Results indicated a potential sensitivity of 82.0% and specificity of 85.5%. The scoring model generalized to a validation subsample (n = 39), producing 85.0% sensitivity and 78.9% specificity. The kappa agreement between predicted and actual group membership was 0.64 (P<.001). Of the 16 subjects unable to complete the call, 11 provided sufficient information to permit us to classify them as impaired. Standard scoring of the interactive voice response-administered Symptoms of Dementia Screener (completed by informants) produced a screening sensitivity of 63.5% and 100% specificity. A lower criterion found a 90.4% sensitivity, without lowering specificity. CONCLUSIONS: Computer-automated telephone screening for early dementia using either informant or direct assessment is feasible. Such systems could provide wide-scale, cost-effective screening, education, and referral services to patients and caregivers.

Aged↗
PubMed2005

Data mining.

Group 14 used data-mining strategies to evaluate a number of issues, including appropriate diagnosis, haplotype estimation, genetic linkage and association studies, and type I error. Methods ranged from exploratory analyses, to machine learning strategies (neural networks, supervised learning, and tree-based methods), to false discovery rate control of type I errors. The general motivations were to find the "story" in the data and to summarize information from a multitude of measures. Several methods illustrated strategies for better trait definition, using summarization of related traits. In the few studies that sought to identify genes for alcoholism, there was little agreement among the different strategies, likely reflecting the complexities of the disease. Nevertheless, Group 14 found that these methods offered strategies to gain a better understanding of the complex pathways by which disease develops.

Alcoholism↗
PubMed1992

The role of computer based techniques in patient monitoring: technical note.

In this paper the requirements of Neurophysiology and Neurotraumatology monitoring are analyzed. As a result a set of designated systems were developed by the authors a short description of which is given in the paper. Finally the future perspectives and problems to be faced are briefly described.

Artificial Intelligence↗
PubMed2005

Learning machines applied to potential forest distribution.

The clearing of forests to obtain land for pasture and agriculture and the replacement of autochthonous species by other faster-growing varieties of trees for timber have both led to the loss of vast areas of forest worldwide. At present, many developed countries are attempting to reverse these effects, establishing policies for the restoration of older woodland systems. Reforestation is a complex matter, planned and carried out by experts who need objective information regarding the type of forest that can be sustained in each area. This information is obtained by drawing up feasibility models constructed using statistical methods that make use of the information provided by morphological and environmental variables (height, gradient, rainfall, etc.) that partially condition the presence or absence of a specific kind of forestation in an area. The aim of this work is to construct a set of feasibility models for woodland located in the basin of the River Liébana (NW Spain), to serve as a support tool for the experts entrusted with carrying out the reforestation project. The techniques used are multilayer perceptron neural networks and support vector machines. Their results will be compared to the results obtained by traditional techniques (such as discriminant analysis and logistic regression) by measuring the degree of fit between each model and the existing distribution of woodlands. The interpretation and problems of the feasibility models are commented on in the Discussion section.

Artificial Intelligence↗
PubMed1994

One vendor's experience: preliminary development of a reminder system based on the Arden Syntax.

This article reviews the efforts of HBO & Company in the production of a first phase clinical alerting system based on the Arden Syntax. The alerting system was integrated with a clinical data repository and clinical workstation to process returning laboratory results. Investigations with expert systems resulted in a C language alerting system. GUI prototyping of an authoring environment led to a Smalltalk language authoring system. Future development is expected to broaden the system scope and address the evolution of the Arden Syntax.

Artificial Intelligence↗