Enriching representation learning using 53 million patient notes through human phenotype ontology embedding.

The Human Phenotype Ontology (HPO) is a dictionary of >15,000 clinical phenotypic terms with defined semantic relationships, developed to standardize phenotypic analysis. Over the last decade, the HPO has been used to accelerate the implementation of precision medicine into clinical practice. In addition, recent research in representation learning, specifically in graph embedding, has led to notable progress in automated prediction via learned features. Here, we present a novel approach to phenotype representation by incorporating phenotypic frequencies based on 53 million full-text health care notes from >1.5 million individuals. We demonstrate the efficacy of our proposed phenotype embedding technique by comparing our work to existing phenotypic similarity-measuring methods. Using phenotype frequencies in our embedding technique, we are able to identify phenotypic similarities that surpass current computational models. Furthermore, our embedding technique exhibits a high degree of agreement with domain experts' judgment. By transforming complex and multidimensional phenotypes from the HPO format into vectors, our proposed method enables efficient representation of these phenotypes for downstream tasks that require deep phenotyping. This is demonstrated in a patient similarity analysis and can further be applied to disease trajectory and risk prediction.

Twin study of age-related macular degeneration.

PURPOSE: To evaluate the genetic contribution in age-related macular degeneration (ARMD) by a disease-ascertained twin study. METHODS: Concordance rates for ARMD in 25 twins were obtained by using four masked graders to confirm the diagnosis of ARMD and place subjects in one of three categories; concordant, intermediate, or discordant. Demographic features and known risk factors for ARMD were compared between monozygotic and dizygotic twin pairs. RESULTS: Of the 25 twin pairs, 15 were monzygotic and 10 were dizygotic. All 15 monozygotic twins were concordant or intermediate for ARMD. Of the dizygotic twin pairs, only one was concordant and five were discordant. In the demographic and risk factor analysis no unusual contributing or confounding variables were detected. CONCLUSIONS: The association between zygosity and concordance for ARMD suggests a major importance for genetics in the etiology of ARMD. Our data further support a multi-factorial, primarily polygenic etiology for the condition.