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

By Robert L. Blum (auth.)

As a medical institution doctor it's very unlikely to flee the inspiration that the tricky clinical difficulties one encounters also are being faced through different' physicians in the course of the global. it really is both obvious that with no particular attempt one's personal sufferer observations are usually not shared with others. with no the scientific literature there will be virtually no significant shadng of expertise. clinical textbooks and journals include studies of the most recent assessments and coverings. from collage hospitals and examine facilities. There are, besides the fact that, certain boundaries to the scientific literature. First. the literature documents just a miniscule and hugely choose component to clinical adventure. moment, due to this selectivity, it can be tricky to use the findings and suggestions within the literature to one's personal sufferers. One severe final result of those features of the scientific literature is that sufferers are principally overtreated. exams and coverings are over-prescribed, and adversarial effe

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

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How might we derive and represent the causal relationships among them using our sample data? First, it is clear that we bring a great deal of prior knowledge to this study. We may have ideas of the mechanisms that produce heart attacks, say arteriosclerosis and/or spasm in the coronary arteries of the heart. We can imagine ways that each of the possible causal factors might lead to one or the other of these. Furthermore, we have notions of the ways that the various factors may inte~relate. For example, it is easy to imagine that people under great occupational stress may experience high blood pressure or that people with sedentary life styles may eat or smoke more.

Below the graph, two interval-events, nephrotic-syndrome and azathioprine, have been plotted as line-drawings indicating corresponding time intervals. The line-drawings of these variables convey the time of occurrence and the intensity or magnitude of the event which they represent. 1) - Hemoglobin---. 1 ) l - - - - . 8) {point-events} Hepatitis _(Intervals <3 , 17) (21 , 26» -(Interval-Values (850 1235») {interval-events} A time-oriented record for one hypothetical patient. Each patient is assigned a consecutive integer for the purpose of identification.

On 2p degrees of freedom, where p is the number of patients. Since the distribution of the scores is known, their. statistical significance may be calculated. Because of autocorrelation, the differences between scores determined at different time lags may not be distributed X2• However, the significances are not taken literally by the Discovery Module, but are merely used to rank the hypotheses in terms of promise. These scores have proven to be a sensitive detector of many causal relationships known to exist in the database on the basis of prior medical knowledge.

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