ObjectivesWe reviewed applications of big data analysis of healthcare and social services in developed countries, and subsequently devised a framework for such an analysis in Korea.MethodsWe reviewed the status of implementing big data analysis of health care and social services in developed countries, and strategies used by the Ministry of Health and Welfare of Korea (Government 3.0). We formulated a conceptual framework of big data in the healthcare and social service sectors at the national level. As a specific case, we designed a process and method of social big data analysis on suicide buzz.ResultsDeveloped countries (e.g., the United States, the UK, Singapore, Australia, and even OECD and EU) are emphasizing the potential of big data, and using it as a tool to solve their long-standing problems. Big data strategies for the healthcare and social service sectors were formulated based on an ICT-based policy of current government and the strategic goals of the Ministry of Health and Welfare. We suggest a framework of big data analysis in the healthcare and welfare service sectors separately and assigned them tentative names: 'health risk analysis center' and 'integrated social welfare service network'. A framework of social big data analysis is presented by applying it to the prevention and proactive detection of suicide in Korea.ConclusionsThere are some concerns with the utilization of big data in the healthcare and social welfare sectors. Thus, research on these issues must be conducted so that sophisticated and practical solutions can be reached.

Background Although vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates. Objective The aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data. Methods The domain and scope of the ontology were determined by developing competency questions. We checked if existing ontologies and conceptual frameworks related to vaccination can be reused for the childhood vaccination ontology. Terms were collected from clinical practice guidelines, research papers, and posts on social media platforms. Class concepts were extracted from these terms. A class hierarchy was developed using a top-down approach. The ontology was evaluated in terms of description logics, face and content validity, and coverage. In total, 40,359 Korean posts on childhood vaccination were collected from 27 social media channels between January and December 2015. Vaccination issues were identified and classified using the second-level class concepts of the ontology. The sentiments were classified in 3 ways: positive, negative or neutral. Posts were analyzed using frequency, trend, logistic regression, and association rules. Results Our childhood vaccination ontology comprised 9 superclasses with 137 subclasses and 431 synonyms for class, attribute, and value concepts. Parent’s health belief appeared in 53.21% (15,709/29,521) of posts and positive sentiments appeared in 64.08% (17,454/27,236) of posts. Trends in sentiments toward vaccination were affected by news about vaccinations. Posts with parents’ health belief , vaccination availability , and vaccination policy were associated with positive sentiments, whereas posts with experience of vaccine adverse events were associated with negative sentiments. Conclusions The childhood vaccination ontology developed in this study was useful for collecting and analyzing social data on childhood vaccination. We expect that practitioners and researchers in the field of childhood vaccination could use our ontology to identify concerns about and sentiments toward childhood vaccination from social data.

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ObjectivesThe aim of this study was to develop and evaluate an obesity ontology as a framework for collecting and analyzing unstructured obesity-related social media posts.MethodsThe obesity ontology was developed according to the ‘Ontology Development 101’. The coverage rate of the developed ontology was examined by mapping concepts and terms of the ontology with concepts and terms extracted from obesity-related Twitter postings. The structure and representative ability of the ontology was evaluated by nurse experts. We applied the ontology to the density analysis of keywords related to obesity types and management strategies and to the sentiment analysis of obesity and diet using social big data.ResultsThe developed obesity ontology was represented by 8 superclasses and 124 subordinate classes. The superclasses comprised ‘risk factors,’ ‘types,’ ‘symptoms,’ ‘complications,’ ‘assessment,’ ‘diagnosis,’ ‘management strategies,’ and ‘settings.’ The coverage rate of the ontology was 100% for the concepts and 87.8% for the terms. The evaluation scores for representative ability were higher than 4.0 out of 5.0 for all of the evaluation items. The density analysis of keywords revealed that the top-two posted types of obesity were abdomen and thigh, and the top-three posted management strategies were diet, exercise, and dietary supplements or drug therapy. Positive expressions of obesity-related postings has increased annually in the sentiment analysis.ConclusionsIt was found that the developed obesity ontology was useful to identify the most frequently used terms on obesity and opinions and emotions toward obesity posted by the geneal population on social media.

As whole genome sequencing (WGS) uncovers variants associated with rare and common diseases, an immediate challenge is to minimize false positive findings due to sequencing and variant calling errors. False positives can be reduced by combining results from orthogonal sequencing methods, but costly. Here we present variant filtering approaches using logistic regression (LR) and ensemble genotyping to minimize false positives without sacrificing sensitivity. We evaluated the methods using paired WGS datasets of an extended family prepared using two sequencing platforms and a validated set of variants in NA12878. Using LR or ensemble genotyping based filtering, false negative rates were significantly reduced by 1.1- to 17.8-fold at the same levels of false discovery rates (5.4% for heterozygous and 4.5% for homozygous SNVs; 30.0% for heterozygous and 18.7% for homozygous insertions; 25.2% for heterozygous and 16.6% for homozygous deletions) compared to the filtering based on genotype quality scores. Moreover, ensemble genotyping excluded > 98% (105,080 of 107,167) of false positives while retaining > 95% (897 of 937) of true positives in de novo mutation (DNM) discovery, and performed better than a consensus method using two sequencing platforms. Our proposed methods were effective in prioritizing phenotype-associated variants, and ensemble genotyping would be essential to minimize false positive DNM candidates.