Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.

OBJECTIVES: Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarboxylase deficiency (AADCd), an autosomal recessive disease caused by pathogenic variants in the dopa decarboxylase gene. Deficiency of the AADC enzyme results in combined severe reductions in monoamine neurotransmitters: dopamine, serotonin, epinephrine, and norepinephrine. This leads to widespread neurological complications affecting motor, behavioral, and autonomic function. The goal of this study was to use EHR data to identify previously undiagnosed patients who may have AADCd without available training cases for the disease. MATERIALS AND METHODS: A multiple symptom and related disease annotated dataset was created and used to train individual concept classifiers on annotated sentence data. A multistep algorithm was then used to combine concept predictions into a single patient rank value. RESULTS: Using an 8000-patient dataset that the algorithms had not seen before ranking, the top and bottom 200 ranked patients were manually reviewed for clinical indications of performing an AADCd diagnostic screening test. The top-ranked patients were 22.5% positively assessed for diagnostic screening, with 0% for the bottom-ranked patients. This result is statistically significant at P < .0001. CONCLUSION: This work validates the approach that large-scale rare-disease screening can be accomplished by combining predictions for relevant individual symptoms and related conditions which are much more common and for which training data is easier to create.

Where are the males? Gender differences in undergraduates' interest in and perceptions of the genetic counseling profession.

Genetic counseling is a female-dominated field, with women comprising about 95% of the profession (Smith et al. 2009). Greater patient choice and satisfaction may be achieved by increasing the number of male counselors, but empirical evidence about the reasons for this gender imbalance is limited. In this study 190 undergraduates (110 females, 79 males, 1 unknown) in upper division bioscience courses completed a survey assessing their knowledge and perceptions of and interest in genetic counseling as a career. There were only two significant gender differences. Females indicated significantly greater interest than males in pursuing a genetic counseling career, and they rated interpersonal skills as more integral to genetic counseling than males. Multiple regression analyses of knowledge and perceptions as possible predictors of male and female interest in pursuing a genetic counseling career yielded no significant predictors of male interest. For females, there were four significant predictors: estimated salary, career characteristics, perceptions of genetic counseling as interpersonally focused, and whether they had already chosen a career. Implications for recruiting males to the profession, and research recommendations are presented.