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PubMed1986

The evaluation of artificial intelligence systems in medicine.

This paper discusses the underlying issues in the evaluation of computer systems which apply artificial intelligence in medicine. Three different levels of evaluation are described: the subjective evaluation of the research contribution of a developmental prototype, the validation of a system's knowledge and performance, and the evaluation of the clinical efficacy of an operational system. The paper outlines a number of evaluation issues at each level, and discusses how previous artificial intelligence in medicine evaluations fit into this framework.

Artificial Intelligence↗
PubMed1995

Knowledge-based temporal abstraction in diabetes therapy.

We suggest a general framework for solving the task of creating abstract, interval-based concepts from time-stamped clinical data. We refer to this problem-solving framework as the knowledge-based temporal-abstraction (KBTA) method. The KBTA method emphasizes explicit representation, acquisition, maintenance, reuse, and the sharing of knowledge required for abstraction of time-oriented clinical data. We describe the subtasks into which the KBTA method decomposes its task, the problem-solving mechanisms that solve these subtasks, and the knowledge necessary for instantiating these mechanisms in a particular clinical domain. We have implemented the KBTA method in the RESUME system and have applied it to the task of monitoring the care of insulin-dependent diabetics.

Artificial Intelligence↗
PubMed1996

[Managed health care: scope, concept and strategic management potentials from the viewpoint of the Swiss Accident Insurance Fund].

The following item presents briefly the cost relevant factors in health care in Switzerland. The principal key issues and reasons for managed health care programs are dealt with by recognizing the new health care law. In order to present the central strategies for improvement in managed care from an integral point of view, managed health care is being treated in the context of a widely founded conceptional framework. A very detailed and medically complete as well as performance oriented case statistic of medical treatments is a strategic success and key factor. The author represents the thesis that the given opportunities and micro-management tools could be much more widely used, that they should be extended and that, therefore, the strength of our independent and liberal health care system could be much better integrated in the context of a necessary social acceptance. This shall not only be reached by maximising the individual revenues, but by optimizing structures and increasing organizational effectiveness in medical health care. The existing readiness of the health care suppliers, especially the physicians, to take over responsibility in improving health care outcome is the core to this strategy.

Artificial Intelligence↗
PubMed1998

Knowledge-based support for the participatory design and implementation of shift systems.

OBJECTIVES: This study developed a knowledge-based software system to support the participatory design and implementation of shift systems as a joint planning process including shift workers, the workers' committee, and management. METHODS: The system was developed using a model-based approach. During the 1st phase, group discussions were repeatedly conducted with 2 experts. Thereafter a structure model of the process was generated and subsequently refined by the experts in additional semistructured interviews. Next, a factual knowledge base of 1713 relevant studies was collected on the effects of shift work. Finally, a prototype of the knowledge-based system was tested on 12 case studies. RESULTS: During the first 2 phases of the system, important basic information about the tasks to be carried out is provided for the user. During the 3rd phase this approach uses the problem-solving method of case-based reasoning to determine a shift rota which has already proved successful in other applications. It can then be modified in the 4th phase according to the shift workers' preferences. The last 2 phases support the final testing and evaluation of the system. The application of this system has shown that it is possible to obtain shift rotas suitable to actual problems and representative of good ergonomic solutions. CONCLUSIONS: A knowledge-based approach seems to provide valuable support for the complex task of designing and implementing a new shift system. The separation of the task into several phases, the provision of information at all stages, and the integration of all parties concerned seem to be essential factors for the success of the application.

Artificial Intelligence↗
PubMed1998

CARE-PARTNER: a computerized knowledge-support system for stem-cell post-transplant long-term follow-up on the World-Wide-Web.

Evidence-based practice in medicine promotes the performance of medicine based upon proven and validated practice. The CARE-PARTNER system presented here is a computerized knowledge-support system for stem-cell post-transplant long-term follow-up (LTFU) care on the WWW, which means that it monitors the quality of the knowledge both of its own knowledge-base and of its users. Its aim is to support the evidence-based practice of the LTFU clinicians and of the home-town physicians who actually care for the transplanted patients. Currently, three fundamental characteristics of CARE-PARTNER are accountable for its knowledge-support function: the quality of its knowledge-base, its availability on the WWW, and its learning from experience capability. As a matter of fact, the integration of a case-based reasoner in the reasoning framework enables the system to introspectively study its results, and to learn from its successes and failures, thus confronting the quality of the guidelines and pathways it reuses to the reality and complexity of the clinical cases.

Artificial Intelligence↗
PubMed2006

A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples.

Currently, the best way to reduce the mortality of cancer is to detect and treat it in the earliest stages. Technological advances in genomics and proteomics have opened a new realm of methods for early detection that show potential to overcome the drawbacks of current strategies. In particular, pattern analysis of mass spectra of blood samples has attracted attention as an approach to early detection of cancer. Mass spectrometry provides rapid and precise measurements of the sizes and relative abundances of the proteins present in a complex biological/chemical mixture. This article presents a review of the development of clinical decision support systems using mass spectrometry from a machine learning perspective. The literature is reviewed in an explicit machine learning framework, the components of which are preprocessing, feature extraction, feature selection, classifier training, and evaluation.

Artificial Intelligence↗
PubMed2000

A java-based application for differential diagnosis of hematopoietic neoplasms using immunophenotyping by flow cytometry.

We describe the implementation of a Java-based application for differential diagnosis of hematopoietic neoplasms using immunophenotyping by flow cytometry. The current version of this Java applet includes the knowledge-base for 33 hematopoietic neoplasms and 43 diagnostic immunophenotyping markers. Java, a new object-oriented computing language, helps facilitate development of this applet, a platform-independent module that can be implemented on the World Wide Web. As the Web rapidly becomes more accessible to users around the world, Web-based software may eventually form the core of decision-support systems in clinical settings. Java-based applications, such as the one described in this paper, are expected to contribute significantly in this area.

Antigens, CD↗
PubMed2001

Acceptance of rules generated by machine learning among medical experts.

OBJECTIVES: The aim was to evaluate the potential for monotonicity constraints to bias machine learning systems to learn rules that were both accurate and meaningful. METHODS: Two data sets, taken from problems as diverse as screening for dementia and assessing the risk of mental retardation, were collected and a rule learning system, with and without monotonicity constraints, was run on each. The rules were shown to experts, who were asked how willing they would be to use such rules in practice. The accuracy of the rules was also evaluated. RESULTS: Rules learned with monotonicity constraints were at least as accurate as rules learned without such constraints. Experts were, on average, more willing to use the rules learned with the monotonicity constraints. CONCLUSIONS: The analysis of medical databases has the potential of improving patient outcomes and/or lowering the cost of health care delivery. Various techniques, from statistics, pattern recognition, machine learning, and neural networks, have been proposed to "mine" this data by uncovering patterns that may be used to guide decision making. This study suggests cognitive factors make learned models coherent and, therefore, credible to experts. One factor that influences the acceptance of learned models is consistency with existing medical knowledge.

Alzheimer Disease↗
PubMed2005

Modelling of classification rules on metabolic patterns including machine learning and expert knowledge.

Machine learning has a great potential to mine potential markers from high-dimensional metabolic data without any a priori knowledge. Exemplarily, we investigated metabolic patterns of three severe metabolic disorders, PAHD, MCADD, and 3-MCCD, on which we constructed classification models for disease screening and diagnosis using a decision tree paradigm and logistic regression analysis (LRA). For the LRA model-building process we assessed the relevance of established diagnostic flags, which have been developed from the biochemical knowledge of newborn metabolism, and compared the models' error rates with those of the decision tree classifier. Both approaches yielded comparable classification accuracy in terms of sensitivity (>95.2%), while the LRA models built on flags showed significantly enhanced specificity. The number of false positive cases did not exceed 0.001%.

Algorithms↗
PubMed1998

Case-based reasoning for medical decision support tasks: the Inreca approach.

We describe an approach for developing knowledge-based medical decision support systems based on the new technology of case-based reasoning. This work is based on the results of the Inreca European project and preliminary results from the Inreca + project which mainly deals with medical applications. One goal was to start from case-based reasoning technology for technical diagnosis and 'scale-up' to more general non-technical decision support tasks as typically given in medical domains. Inreca technology has been used to build an initial decision support system at the Russian Toxicology Information and Advisory Center in Moscow for diagnosing poison cases caused by psychotropes.

Artificial Intelligence↗
PubMed2001

Cooperation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: possible implications for cognition, consciousness, intelligence and creativity.

It is suggested that the anatomical structures which mediate consciousness evolved as decisive embellishments to a (non-conscious) design strategy present even in the simplest unicellular organisms. Consciousness is thus not the pinnacle of a hierarchy whose base is the primitive reflex, because reflexes require a nervous system, which the single-celled creature does not possess. By postulating that consciousness is intimately connected to self-paced probing of the environment, also prominent in prokaryotic behavior, one can make mammalian neuroanatomy amenable to dramatically straightforward rationalization. Muscular contraction is the nervous system's only externally directed product, and the signaling routes which pass through the various brain components must ultimately converge on the motor areas. The function of several components is still debatable, so it might seem premature to analyze the global operation of the circuit these routes constitute. But such analysis produces a remarkably simple picture, and it sheds new light on the roles of the individual components. The underlying principle is conditionally permitted movement, some components being able to veto muscular contraction by denying the motor areas sufficient activation. This is true of the basal ganglia (BG) and the cerebellum (Cb), which act in tandem with the sensory cerebrum, and which can prevent the latter's signals to the motor areas from exceeding the threshold for overt movement. It is also true of the anterior cingulate, which appears to play a major role in directing attention. In mammals, the result can be mere thought, provided that a second lower threshold is exceeded. The veto functions of the BG and the Cb stem from inhibition, but the countermanding disinhibition develops at markedly different rates in those two key components. It develops rapidly in the BG, control being exercised by the amygdala, which itself is governed by various other brain regions. It develops over time in the Cb, thereby permitting previously executed movements that have proved advantageous. If cognition is linked to overt or covert movement, intelligence becomes the ability to consolidate individual motor elements into more complex patterns, and creativity is the outcome of a race-to-threshold process which centers on the motor areas. Amongst the ramifications of these ideas are aspects of cortical oscillations, phantom limb sensations, amyotrophic lateral sclerosis (ALS) the difficulty of self-tickling and mirror neurons.

Animals↗
PubMed1999

Supporting decisions in diabetic patients management through case-based retrieval.

In this paper we present a tool for the intelligent retrieval of past cases to support Insulin Dependent Diabetes Mellitus patients therapy revision. This tool was designed for assisting physicians during periodical monitoring visits. Each case is represented through i) a collection of features that describe the patient's clinical state at the decision time, i.e. the control visit, ii) the decision taken in terms of therapy revision and iii) the outcome obtained on the metabolic control during the following monitoring period. A new case is first classified into a protypical monitoring situation, and then similar cases are retrieved and shown to the user. This tool is fully integrated in a Web-based distributed system for Diabetes Management, developed within in the EU project T-IDDM.

Decision Support Techniques↗
PubMed1991

Reaction times and intelligence: a comparison of Japanese and British children.

Japanese and British 9-year-old children were compared on the standard progressive matrices and twelve reaction time parameters providing measures of simple and complex decision times, movement times and variabilities. The mean of the Japanese children on the progressive matrices exceeded that of the British children by 0.65 SD units and on the decision times component of reaction times by 0.50 SD units, suggesting that the high Japanese mean on psychometric intelligence is largely explicable in terms of the more efficient processing of information at the neurological level. Japanese children also showed faster movement times but, contrary to expectation, had greater variabilities than British children.

Child↗