Artificial intelligence in pediatrics: important clinical signs in newborn syndromes

New methods are warranted in the field of syndromology. This 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% 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%. 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 does not require prior knowledge of diagnoses and will make a tree of syndromes based upon clinical signs. A 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.