A retrospective analysis of administrative claims containing a diverse mixture of ages, ethnicities, and geographical regions across the United States was conducted in order to identify medical events that occur during pregnancy and are associated with autism spectrum disorder (ASD). The dataset used in this study is comprised of 123,824 pregnancies of which 1265 resulted in the child being diagnosed with ASD during the first five years of life. Logistic regression analysis revealed significant relationships between several maternal medical claims, made during her pregnancy and segmented by trimester, and the child’s diagnosis of ASD. Having a biological sibling with ASD, maternal use of antidepressant medication and psychiatry services as well as non-pregnancy related claims such hospital visits, surgical procedures, and radiology exposure were related to an increased risk of ASD regardless of trimester. Urinary tract infections during the first trimester and preterm delivery during the second trimester were also related to an increased risk of ASD. Preventative and obstetrical care were associated with a decreased risk for ASD. A better understanding of the medical factors that increase the risk of having a child with ASD can lead to strategies to decrease risk or identify those children who require increased surveillance for the development of ASD to promote early diagnosis and intervention.

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Biomarkers offer significant potential for diagnosis and treatment of complex disorders such as asthma, epilepsy, autism, Parkinson’s, and Alzheimer’s, as well as many others. In many cases, however, there is little consensus on what an appropriate biomarker would be. Consequently, biomarker identification is an important area of research for which a link between physiological measurements and the presence/absence or severity of a disorder can be established. This is nontrivial due to both the curse of dimensionality and because the number of measurements per trial often exceeds the number of trial participants. Overfitting of potential biomarkers is thus a significant problem that needs to be addressed. This paper highlights similarities between the biomarker identification problem and the parameter estimation problem, more specifically the regularization used for avoiding overfitting. Parallels between the underlying methodologies are pointed out and opportunities for advancing the systems’ concepts are discussed. Finally, a candidate biomarker for diagnosis of autism spectrum disorder is identified from a data set comprising metabolic measurements from four separate clinical trials to illustrate the procedure outlined in this work.