| Abstract | New methods are warranted in the field of syndromology. The study is an exploration into whether an artificial intelligence method (ID3) could provide a new angle for approaching syndromes. Diagnosing syndromes in the newborn is difficult. The accepted approach is to look for individual clinical signs that add up to a syndrome diagnosis. Of all possible clinical signs, one would want to extract the signs with the strongest predictive power. I used the ID3 algorithm to extract predictive clinical signs from a catalogue of syndromes (Birth Defects Encyclopedia OnLine; BDEO). Using information from BDEO, files of randomly generated "patients" were created. THe signs consistently high in the identification tree were long philtrum, short palpebral fissures, low-set ears, and hepatosplenomegaly. The program used featured a crude "expert system" based on the ID3 algorithm. When using one-half of the data set as a training set and the other half as a testbed, a correct classification rate of 92.1-98.1 percent was attained. When the ID3 expert system was tested against cases from a clinical database (Pictures of Standard Syndromes and Undiagnosed Malformations), the correct classification rate was less than 20 percent. This may not necessarily reflect faults with the ID3 approach, but possibly biases in the clinical database. In syndromology no "criterion standards" exist that can confirm a diagnosis. The statistical method of cluster analysis was performed as a validity check to provide a tree for comparison with the ID3 tree. There was a reasonable degree of agreement between the two. Applying artificial intelligence methods to this field highlights problems with basic assumptions and philosophical aspects of syndrome diagnosis. |