Evaluation of Outcomes of the Busan Community-based Palliative Care Project in Korea.

PURPOSE: This study aimed to evaluate the outcomes of a community-based palliative care project conducted in Busan city, Korea, from 2013 to 2015. METHODS: We selected four outcome indices based on the project's outcomes derived from a logic model and used a longitudinal and cross-sectional comparative design approach depending on the outcome index. RESULTS: The utilization rate of palliative care increased from 9.2% in 2012 to 41.9% in 2015. Regarding symptom changes in 65 patients receiving palliative care at 3 and 6 months (mean age = 72 years, standard deviation = 9.64, 55.4% women), pain, anxiety, and depression had improved. Quality of life was higher among palliative care patients compared with patients who did not receive palliative care (t = 2.09, p = .039). Regarding recognition of palliative care, civil servants at public health centers who participated in the pilot project (2013-2014) scored higher than those at public health centers who began participation in 2015 (t = 2.67, p = .008). CONCLUSION: This is the first study in Korea that systematically evaluated community-based palliative care. The Busan Community-based Palliative Care Project improved the quality of life of palliative care patients by providing services at an appropriate level and by raising the recognition of palliative care in the community. To increase the utilization ratio of palliative care and the quality of service, strategies should be developed to supplement medical support systems.

CASS: A distributed network clustering algorithm based on structure similarity for large-scale network.

As the size of networks increases, it is becoming important to analyze large-scale network data. A network clustering algorithm is useful for analysis of network data. Conventional network clustering algorithms in a single machine environment rather than a parallel machine environment are actively being researched. However, these algorithms cannot analyze large-scale network data because of memory size issues. As a solution, we propose a network clustering algorithm for large-scale network data analysis using Apache Spark by changing the paradigm of the conventional clustering algorithm to improve its efficiency in the Apache Spark environment. We also apply optimization approaches such as Bloom filter and shuffle selection to reduce memory usage and execution time. By evaluating our proposed algorithm based on an average normalized cut, we confirmed that the algorithm can analyze diverse large-scale network datasets such as biological, co-authorship, internet topology and social networks. Experimental results show that the proposed algorithm can develop more accurate clusters than comparative algorithms with less memory usage. Furthermore, we confirm the proposed optimization approaches and the scalability of the proposed algorithm. In addition, we validate that clusters found from the proposed algorithm can represent biologically meaningful functions.

Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles.

The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair represents a significant gene-gene interaction (GGI). In order to solve this problem, we proposed a random forest-based method to classify significant GGIs from gene expression data. To train the model, we defined novel feature sets and utilised various high-confidence interactome datasets to deduce the correct answer set from known disease-specific genes. Using Alzheimer's disease data, the proposed method showed remarkable accuracy, and the GGIs established in the analysis can be used to build a meaningful genetic network that can explain the mechanisms underlying Alzheimer's disease.