• Perspectives in Nursing Science
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• 제5권1호
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• pp.45-58
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• 2008

The nursing informatics has been booming in Taiwan since 2003 when we started to use the end user computing strategy to promote it. We used Excel 2003, which was well known and used by our clinical nurses, as well as the embedded VBA to teach them how simple information applications could and should be built to meet their information management needs in order to support their professional responsibility. Many cost-effective projects were successfully done and the importance and potentials of nursing informatics started to be noticed. Our training strategy and materials are introduced in this paper.

• Journal of Computing Science and Engineering
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• 제11권4호
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• pp.109-110
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• 2017

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• Genomics & Informatics
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• 제20권1호
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• pp.14.1-14.4
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• 2022

Permutation testing is a robust and popular approach for significance testing in genomic research that has the advantage of reducing inflated type 1 error rates; however, its computational cost is notorious in genome-wide association studies (GWAS). Here, we developed a supercomputing-aided approach to accelerate the permutation testing for GWAS, based on the message-passing interface (MPI) on parallel computing architecture. Our application, called MPI-GWAS, conducts MPI-based permutation testing using a parallel computing approach with our supercomputing system, Nurion (8,305 compute nodes, and 563,740 central processing units [CPUs]). For 10⁷ permutations of one locus in MPI-GWAS, it was calculated in 600 s using 2,720 CPU cores. For 10⁷ permutations of ~30,000-50,000 loci in over 7,000 subjects, the total elapsed time was ~4 days in the Nurion supercomputer. Thus, MPI-GWAS enables us to feasibly compute the permutation-based GWAS within a reason-able time by harnessing the power of parallel computing resources.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.34-39
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• 2021

Collaborative ontology construction is the latest trend in developing ontologies. In this technique domain specialists and ontologists need to work together. Because of the complexity associated with ontology construction, it's done in an iterative and incremental fashion. After each iteration, an ontology increment will be produced. Current ontology increment is always an enhanced version of the previous increment. Each ontology increment has to be verified for its accuracy. Domain specialists' contribution is very significant in accomplishing this necessity. Unfortunately, non-computing domain specialists (i.e. medical doctors, bankers, lawyers) are illiterate on semantic concepts. Therefore, validating the accuracy of the ontology increment is a complex hurdle for them. This research proposes verbalization approach to address this complexity.

• Genomics & Informatics
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• 제19권2호
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• pp.17.1-17.13
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• 2021

Breast cancer is one of the leading causes of cancer in women all over the world and accounts for ~25% of newly observed cancers in women. Epigenetic modifications influence differential expression of genes through non-coding RNA and play a crucial role in cancer regulation. In the present study, epigenetic regulation of gene expression by in-silico analysis of histone modifications using chromatin immunoprecipitation sequencing (ChIP-Seq) has been carried out. Histone modification data of H3K4me3 from one normal-like and four breast cancer cell lines were used to predict miRNA expression at the promoter level. Predicted miRNA promoters (based on ChIP-Seq) were used as a probe to identify gene targets. Five triple-negative breast cancer (TNBC)-specific miRNAs (miR153-1, miR4767, miR4487, miR6720, and miR-LET7I) were identified and corresponding 13 gene targets were predicted. Eight miRNA promoter peaks were predicted to be differentially expressed in at least three breast cancer cell lines (miR4512, miR6791, miR330, miR3180-3, miR6080, miR5787, miR6733, and miR3613). A total of 44 gene targets were identified based on the 3'-untranslated regions of downregulated mRNA genes that contain putative binding targets to these eight miRNAs. These include 17 and 15 genes in luminal-A type and TNBC respectively, that have been reported to be associated with breast cancer regulation. Of the remaining 12 genes, seven (A4GALT, C2ORF74, HRCT1, ZC4H2, ZNF512, ZNF655, and ZNF608) show similar relative expression profiles in large patient samples and other breast cancer cell lines thereby giving insight into predicted role of H3K4me3 mediated gene regulation via the miRNA-mRNA axis.

• 한국컴퓨터정보학회논문지
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• 제19권5호
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• pp.27-35
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• 2014

오늘날의 의료산업은 첨단 IT에 기반으로 언제, 어디서나 사용 및 접근이 가능한 인간중심의 U-헬스케어 패러다임으로 대변될 수 있다. 또한의료산업은정보통신기술발달을기반으로많은발전 및 응용에 관한 연구가 활발히 진행되고 있다. 의료정보시스템은 의료 IT 정보 시스템들이 통합되는 방향으로 진화해가면서 발전된 IT와 융합한 u-헬스케어 시스템 구축을 목표로 하고 있다. 따라서 이 논문에서는 분산된 의료정보 시스템을 이용해 자유로운u-헬스케어 서비스를 제공한다. 제안 시스템은 상호 이질적인 개인의료정보 데이터인 PHR을 하나로 통합하여 IaaS 클라우드 컴퓨팅 기반으로 운영관리 한다. 제안 시스템은 PHR을 기존 웹기반으로 통합 운영하는 방법보다 접근시간은 24%, 서비스 처리를 위한 대기시간은 4.3% 향상됨을 실험을 통해 증명하였다.

• International Journal of Contents
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• 제9권1호
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• pp.11-17
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• 2013

Recently, online real-time web accessible mobile devices such as smartphones, tablet PCs and application services have been developing rapidly. Hospitals also need to adopt an efficient information system that can provide decent medical services under mobile computing environments to complete future medical services. This study proposes a system in which a doctor can examine a patient and make out a prescription in a ward on a real-time based on the current Order Communication Systems (OCSs) through mobile interfaces. The proposed system implements mobile web pages using HTML5, instead of a mobile app. Web processing speed can be enhanced using the web socket and web storage functions of HTML5.

• Journal of Computing Science and Engineering
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• 제11권4호
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• pp.130-141
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• 2017

Feature selection is one of the most challenging problems of pattern recognition and data mining. In this paper, a feature selection algorithm based on an improved version of binary differential evolution is proposed. The method simultaneously optimizes two feature selection criteria, namely, set approximation accuracy of rough set theory and relational algebra based derived score, in order to select the most relevant feature subset from an entire feature set. Superiority of the proposed method over other state-of-the-art methods is confirmed by experimental results, which is conducted over seven publicly available benchmark datasets of different characteristics such as a low number of objects with a high number of features, and a high number of objects with a low number of features.

• Genomics & Informatics
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• 제20권1호
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• pp.3.1-3.10
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• 2022

The recent development of whole-genome sequencing technologies paved the way for understanding the genomes of microorganisms. Every whole-genome sequencing (WGS) project requires a considerable cost and a massive effort to address the questions at hand. The final step of WGS is data analysis. The analysis of whole-genome sequence is dependent on highly sophisticated bioinformatics tools that the research personal have to buy. However, many laboratories and research institutions do not have the bioinformatics capabilities to analyze the genomic data and therefore, are unable to take maximum advantage of whole-genome sequencing. In this aspect, this study provides a guide for research personals on a set of bioinformatics tools available online that can be used to analyze whole-genome sequence data of bacterial genomes. The web interfaces described here have many advantages and, in most cases exempting the need for costly analysis tools and intensive computing resources.