Knowledge, awareness and attitude of eye donation among non-clinical staff of a tertiary eye hospital in South India.

Purpose: This study aimed to evaluate the knowledge, awareness and attitude of eye donation among non-clinical staff of tertiary eye hospitals and to convey a positive attitude toward eye donation by enhancing their awareness and knowledge. Methods: An online cross-sectional study was conducted among the non-clinical staff from all centers of a tertiary eye care hospital across Tamil Nadu. Quiz link was emailed to non-clinical staff of all the centers. On completion of the quiz, the participants viewed their respective scores and the correct answers to all questions. This activity was presumed to subsequently improve their knowledge and clear up the myths on eye donation. Results: Two hundred twenty-eight non-clinical staff from 11 hospitals participated in the quiz. Mean age was 35.3 ± 9.8 years and 130 were female staff (57.05%). One hundred eighty-one participants (79.39%) scored over 50% of the total 17 queries. One hundred eighty-six (81.58%) and 142 (62.28%) participants scored over 50% in the awareness section and knowledge section, respectively. Eye bank volunteers (73, 32.02%) were the main source of information. Twenty-four (10.53%) had already taken pledge for eye donation and 175 (76.75%) were willing to pledge, 29 (12.72%) were not willing to pledge. Twenty-two out of these 29 (75.86%) had no specific reason for not pledging. Family, religious reasons, lack of clarity and fear were least cited reasons (13.79%). Conclusion: Non-clinical staff of an eye hospital are easily approachable and are expected to be more knowledgeable by the general public around them. They might act as primary motivators in raising awareness within their family, friends, relatives and neighbors.

Phenotype Concept Set Construction from Concept Pair Likelihoods.

Phenotyping algorithms are essential tools for conducting clinical research on observational data. Manually devel- oped phenotyping algorithms, such as those curated within the eMERGE (electronic Medical Records and Genomics) Network, represent the gold standard but are time consuming to create. In this work, we propose a framework for learning from the structure of eMERGE phenotype concept sets to assist construction of novel phenotype definitions. We use eMERGE phenotypes as a source of reference concept sets and engineer rich features characterizing the con- cept pairs within each set. We treat these pairwise relationships as edges in a concept graph, train models to perform edge prediction, and identify candidate phenotype concept sets as highly connected subgraphs. Candidate concept sets may then be interrogated and composed to construct novel phenotype definitions.