• Genomics & Informatics
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• v.11 no.4
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• pp.186-190
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• 2013

The advances in electronic medical records (EMRs) and bioinformatics (BI) represent two significant trends in healthcare. The widespread adoption of EMR systems and the completion of the Human Genome Project developed the technologies for data acquisition, analysis, and visualization in two different domains. The massive amount of data from both clinical and biology domains is expected to provide personalized, preventive, and predictive healthcare services in the near future. The integrated use of EMR and BI data needs to consider four key informatics areas: data modeling, analytics, standardization, and privacy. Bioclinical data warehouses integrating heterogeneous patient-related clinical or omics data should be considered. The representative standardization effort by the Clinical Bioinformatics Ontology (CBO) aims to provide uniquely identified concepts to include molecular pathology terminologies. Since individual genome data are easily used to predict current and future health status, different safeguards to ensure confidentiality should be considered. In this paper, we focused on the informatics aspects of integrating the EMR community and BI community by identifying opportunities, challenges, and approaches to provide the best possible care service for our patients and the population.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.204-214
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• 2017

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.123-141
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• 2019

The goal of this study is to review the major research trend on the convergence studies of AI and healthcare technologies. For the study, 15,260 English articles on AI and healthcare related topics were collected from Scopus for 55 years from 1963, and text mining techniques were conducted. As a result, seven key research topics were defined : "AI for Clinical Decision Support System (CDSS)", "AI for Medical Image", "Internet of Healthcare Things (IoHT)", "Big Data Analytics in Healthcare", "Medical Robotics", "Blockchain in Healthcare", and "Evidence Based Medicine (EBM)". The result of this study can be utilized to set up and develop the appropriate healthcare R&D strategies for the researchers and government. In this study, text mining techniques such as Text Analysis, Frequency Analysis, Topic Modeling on LDA (Latent Dirichlet Allocation), Word Cloud, and Ego Network Analysis were conducted.

• Clinics in Shoulder and Elbow
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• v.26 no.1
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• pp.55-63
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• 2023

Background: Common questions about shoulder arthroplasty (SA) searched online by patients and the quality of this content are unknown. The purpose of this study is to uncover questions SA patients search online and determine types and quality of webpages encountered. Methods: The "People also ask" section of Google Search was queried to return 900 questions and associated webpages for general, anatomic, and reverse SA. Questions and webpages were categorized using the Rothwell classification of questions and assessed for quality using the Journal of the American Medical Association (JAMA) benchmark criteria. Results: According to Rothwell classification, the composition of questions was fact (54.0%), value (24.7%), and policy (21.3%). The most common webpage categories were medical practice (24.6%), academic (23.2%), and medical information sites (14.4%). Journal articles represented 8.9% of results. The average JAMA score for all webpages was 1.69. Journals had the highest average JAMA score (3.91), while medical practice sites had the lowest (0.89). The most common question was, "How long does it take to recover from shoulder replacement?" Conclusions: The most common questions SA patients ask online involve specific postoperative activities and the timeline of recovery. Most information is from low-quality, non-peer-reviewed websites, highlighting the need for improvement in online resources. By understanding the questions patients are asking online, surgeons can tailor preoperative education to common patient concerns and improve postoperative outcomes. Level of evidence: IV.

• Health Policy and Management
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• v.30 no.1
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• pp.82-91
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• 2020

Background: This study investigates the influence factors of medical service variations using medical charge and the length of stay (LOS) for urinary incontinence surgery and uterine polypectomy. Methods: The National Health Insurance claims data and Medical Resource Report by the Health Insurance Review & Assessment Service in 2016 were used. Frequency analysis, one-way analysis of variance, and Bonferroni post-hoc tests were executed for each surgery. A multilevel analysis was executed to assess the factors to the medical charge and LOS for each surgery in patient, doctor, and hospital level. Results: Fifty-two point eight percent of urinary incontinence surgery and 87.1% of uterine polypectomy were distributed in general and tertiary hospitals. Among three levels, the patient level variation was 61.5% or 77.2% in medical charge and 93.9% or 96.3% in LOS, respectively. The doctor level variation was 29.6% or 22.6% in medical charge and 0.6% or 0.0% in LOS, respectively. The institution level variation was 8.9% or 0.2% in medical charge and 5.5% or 3.7% in LOS, respectively. Number of other disease and organizational type were main factors that affected the charge and LOS for urinary incontinence surgery and uterine polypectomy. Conclusion: Medical service variations of the urinary incontinence surgery and uterine polypectomy were the largest for the patient level, followed by doctor level for the medical charge, and the institution level for the LOS.

• Korean Chemical Engineering Research
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• v.44 no.1
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• pp.10-15
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• 2006

Nanobiotechnology has attracted increasing interest during the last 10 years. Particularly in the fields of medicine, drug discovery, and pharmacology, this area of research has opened up new perspectives in analytics and therapy. Nanobiotechnolgy is a typical interdisciplinary field of research, and is based on the cooperative work of biologists, chemists, physicists, engineers, and medical doctors. This review article describes recent research and development of nanobiotechnology including nanobioanalysis, nanobiochip/sensor and nanobiomaterials.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.166-174
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• 2021

A large volume of patient data is generated from various devices used in healthcare applications. With increase in the volume of data generated in the healthcare industry, more wellness monitoring is required. A cloud-enabled analysis of healthcare data that predicts patient risk factors is required. Machine learning techniques have been developed to address these medical care problems. A novel technique called the radix-trie-based Tanimoto kernel regressive infomax boost classification (RT-TKRIBC) technique is introduced to analyze the heterogeneous health data in the cloud to predict the health risks and send alerts. The infomax boost ensemble technique improves the prediction accuracy by finding the maximum mutual information, thereby minimizing the mean square error. The performance evaluation of the proposed RT-TKRIBC technique is realized through extensive simulations in the cloud environment, which provides better prediction accuracy and less prediction time than those provided by the state-of-the-art methods.

• Korea Journal of Hospital Management
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• v.23 no.3
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• pp.52-62
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• 2018

This study aims to analyze the factors affecting in-hospital complication and length of stay in elderly patients with total knee arthroplasty. A total of 8,224 inpatients over 65 years old were selected from the national old inpatient sample data which was produced by Health Insurance Review and Assessment Service in 2016. STATA 12.0 was performed using frequency, chi-square test, t-test, ANOVA and multiple linear and logistic regression analysis. Analysis results show that ages(over 85), Charlson Comorbidity Index, district(metropolitan) for general hospitals and gender, district, beds(100-199) for hospitals are significantly influenced in-hospital complication. Statistically significant factors affecting the length of stay are gender, insurance type, depression, district, bed(300 over) for general hospitals and gender, type of insurance, Charlson Comorbidity Index, depression, district, beds(200-299) for hospitals. Based on these findings, the factors affecting in-hospital complication and length of stay were different depending on the type of medical institution. Accordingly, policymakers should analyze the differences in care behavior depending on the type of medical institution and expand policy and financial support to resolve them.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1014-1025
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• 2020

Cloud computing services changed the way the data are managed across the healthcare system that can improve patient care. Currently, most healthcare organizations are using cloud-based applications and related services to deliver better healthcare facilities. But architecting a cloud-based healthcare system needs deep knowledge about the working nature of these services and the requirements of the healthcare environment. The success is based on the usage of appropriate cloud services in the architecture to manage the data flow across the healthcare system.Cloud service providers offer a wide variety of services to ingest, store and process healthcare data securely. The top three public cloud providers- Amazon, Google, and Microsoft offers advanced cloud services for the solution that the healthcare industry is looking for. This article proposes a framework that can effectively utilize cloud services to handle the data flow among the various stages of the healthcare infrastructure. The useful cloud services for ingesting, storing and analyzing the healthcare data for the proposed framework, from the top three cloud providers are listed in this work. Finally, a cloud-based healthcare architecture using Amazon Cloud Services is constructed for reference.