Download Discovery and Representation of Causal Relationships from a by Robert L. Blum (auth.) PDF

By Robert L. Blum (auth.)

As a clinic doctor it really is very unlikely to flee the idea that the tricky clinical difficulties one encounters also are being faced by way of different' physicians during the international. it really is both obvious that with no specified attempt one's personal sufferer observations should not shared with others. with out the scientific literature there will be virtually no significant shadng of expertise. clinical textbooks and journals include stories of the newest checks and coverings. from collage hospitals and study facilities. There are, although, yes barriers to the clinical literature. First. the literature files just a miniscule and hugely choose section of scientific adventure. moment, due to this selectivity, it can be tough to use the findings and suggestions within the literature to one's personal sufferers. One severe end result of those features of the scientific literature is that sufferers are principally overtreated. exams and coverings are over-prescribed, and opposed effe

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Extra resources for Discovery and Representation of Causal Relationships from a Large Time-Oriented Clinical Database: The RX Project

Example text

1. Causal Models: An Overview Causal models· of the kind used· in the RX knowledge base are currently of great theoretical and practical interest in economics. psychology. biology. and sociology [Bentler 80] [Kenny 79]. There is also considerable interest in causal models in the field of artificial intelligence (AI) [de Kleer 81] [Rieger 77] [Weiss 78]. a subdiscipline of computer science. The difference in form between the causal models developed by economists and psychologists and those of computer scientists is a reflection of the nature of the phenomena they have tried to model.

No link is ever regarded as being beyond refutation at some future time. However, the readiness with which RX overwrites links in the KB'is a function of the validity of the link. In some cases it may be more appropriate for the tech writer to summarize a bimodal relationship as two directed arcs joining a cause and an effect. Consider the medical consequences of a bee sting. Almost always the reaction is transient and mild. Rarely the reaction is severe and fatal. Clearly, there are instances when a more elaborate syntax of causal relationships might be desirable.

The systems modeled by social scientists are largely ones that evolved naturally: human form and function and human societies and economies. for example. The systems that AI scientists have tended to model are largely manmade: computers. electrical circuits. mechanical devices. for example. Although both natural and manmade systems may be advantageously described by causal models. there is a crucial distinction between them that must be reflected in the models. Natural systems are inherently probabilistic.

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