Objectives: Skin cancer is a prevalent type of malignancy, necessitating efficient diagnostic tools. This study aimed to develop an automated skin lesion classification model using the dynamically expandable representation (DER) incremental learning algorithm. This algorithm adapts to new data and expands its classification capabilities, with the goal of creating a scalable and efficient system for diagnosing skin cancer. Methods: The DER model with incremental learning was applied to the HAM10000 and ISIC 2019 datasets. Validation involved two steps: initially, training and evaluating the HAM10000 dataset against a fixed ResNet-50; subsequently, performing external validation of the trained model using the ISIC 2019 dataset. The model’s performance was assessed using precision, recall, the F1-score, and area under the precision-recall curve. Results: The developed skin lesion classification model demonstrated high accuracy and reliability across various types of skin lesions, achieving a weighted-average precision, recall, and F1-score of 0.918, 0.808, and 0.847, respectively. The model’s discrimination performance was reflected in an average area under the curve (AUC) value of 0.943. Further external validation with the ISIC 2019 dataset confirmed the model’s effectiveness, as shown by an AUC of 0.911. Conclusions This study presents an optimized skin lesion classification model based on the DER algorithm, which shows high performance in disease classification with the potential to expand its classification range. The model demonstrated robust results in external validation, indicating its adaptability to new disease classes.

Recent rapid advances in artificial intelligence (AI), especially in deep learning methods, have produced meaningful results in many areas. However, to achieve meaningful results for healthcare through AI, it is important to understand the meaning and characteristics of data in that area. For medical AI, a simple approach that accumulates massive amounts of data based on existing big data concepts cannot provide meaningful results in the healthcare field. We need well-curated data as opposed to a simple aggregation of data. The purpose of this study is to present the types and characteristics of healthcare data and future directions for the successful combination of AI and medical care.
Artificial Intelligence* ; Delivery of Health Care* ; Korea* ; Learning ; Machine Learning

DNA microarray is a high-throughput biomedical technology that monitors gene expression for thousands of genes in parallel. The abundance and complexity of the gene expression data have given rise to a requirement for their systematic management and analysis to support many laboratories performing microarray research. On these demands, we developed Xperanto for integrated data management and analysis using user-friendly web-based interface. Xperanto provides an integrated environment for management and analysis by linking the computational tools and rich sources of biological annotation. With the growing needs of data sharing, it is designed to be compliant to MGED (Microarray Gene Expression Data) standards for microarray data annotation and exchange. Xperanto enables a fast and efficient management of vast amounts of data, and serves as a communication channel among multiple researchers within an emerging interdisciplinary field.
Biomedical Technology ; DNA* ; Gene Expression ; Information Dissemination ; Oligonucleotide Array Sequence Analysis*

With growing needs of microarray data sharing, there are efforts for the development of microarray standards. The standard data exchange model, MAGE-OM (Microarray Gene Expression Object Model) is an object-oriented conceptual model for microarray expression data. MAGE-OM database system is applicable for storage of the associated XML data exchange format MAGE-ML (Microarray Gene Expression Markup Language) and for higher level analysis and integration with biomedical resources. We have implemented MAGE-OM in both frame-based ontology and relational database to exploit the great modeling power of MAGE-OM and compared them in terms of consistency, efficiency and flexibility to the data model. Two implementations showed considerable difference in representing relationships among classes. The ontology in the frame-based system nearly matched the object-oriented model, but performance may become problematic as the database grows. The relational database schema was preferable for performance but it is difficult to guarantee the consistency to the conceptual object level. Our relational schema is also shown to be simplified and provide improved efficiency in comparison with recently published database Array Express at the European Bioinformatics Institute. These design approaches would be helpful to understand the suitability and limitations of each implementation in the context of building standard-compliant database for microarray.
Computational Biology ; Gene Expression ; Information Dissemination ; Pliability

Objective: Early detection and intervention of developmental disabilities (DDs) are critical to improving the long-term outcomes of afflicted children. In this study, our objective was to utilize facial landmark features from mobile application to distinguish between children with DDs and typically developing (TD) children. Methods: The present study recruited 89 children, including 33 diagnosed with DD, and 56 TD children. The aim was to examine the effectiveness of a deep learning classification model using facial video collected from children through mobile-based application. The study participants underwent comprehensive developmental assessments, which included the child completion of the Korean Psychoeducational Profile-Revised and caregiver completing the Korean versions of Vineland Adaptive Behavior Scale, Korean version of the Childhood Autism Rating Scale, Social Responsiveness Scale, and Child Behavior Checklist. We extracted facial landmarks from recorded videos using mobile application and performed DDs classification using long short-term memory with stratified 5-fold cross-validation. Results: The classification model shows an average accuracy of 0.88 (range: 0.78–1.00), an average precision of 0.91 (range: 0.75–1.00), and an average F1-score of 0.80 (range: 0.60–1.00). Upon interpreting prediction results using SHapley Additive exPlanations (SHAP), we verified that the most crucial variable was the nodding head angle variable, with a median SHAP score of 2.6. All the top 10 contributing variables exhibited significant differences in distribution between children with DD and TD (p<0.05). Conclusion The results of this study provide evidence that facial landmarks, utilizing readily available mobile-based video data, can be used to detect DD at an early stage.

OBJECTIVE: Gene expression microarrays become a widely used tool in biomedicine. With growing needs of microarray data sharing, there are efforts for the development of microarray standards. MAGE-OM(Microarray Gene Expression Object Model) is a data exchange model and MAGE-ML is an XML-based data exchange format. Most database, however, do not have a suitable structure for MAGE-ML storage and maximum use of the data. Therefore, we have created relational database implementing MAGE-OM for the storage of MAGE-ML with importing and exporting capabilities. METHODS: A relational schema is derived from MAGE-OM with simple object-relational mapping strategy to reduce complexity of MAGE-OM. Data transfer between database and MAGE-ML document is performed via MAGE-OM using the MAGE Software Toolkit(MAGEstk). RESULTS: Our database accepts microarray data as MAGE-ML files through web-based interface, classifying into two types of submission, array or experiment. MAGE-ML import-export function is flexible to accommodate changing data model by separating model definition and implementation layers. CONCLUSION: Standard-based implementation of gene expression database enhances the collection and the structured storage of large-scale gene expression data from heterogeneous data sources.
Information Storage and Retrieval ; Gene Expression* ; Information Dissemination

The propensity score is defined as the probability of each individual study subject being assigned to a group of interest for comparison purposes. Propensity score adjustment is a method of ensuring an even distribution of confounders between groups, thereby increasing between group comparability. Propensity score analysis is therefore an increasingly applied statistical method in observational studies. The purpose of this article was to provide a step-by-step nonmathematical conceptual guide to propensity score analysis with particular emphasis on propensity score matching. A software program code used for propensity score matching was also presented.
Female ; Humans ; Male ; Middle Aged ; *Propensity Score ; Radiology/*methods ; Research Design ; Research Personnel ; Software

OBJECTIVE: HL7(Health Level 7) develops standards for the representation of clinical documents like discharge and consultation notes. The goal of the present study is to develop XML(eXtensible Markup Language)-based communication standard for discharge note. METHODS: This paper presents the use of XML for electronic communication in a document-based EMR, first, as a format for the exchange of structured message, and second, as a comprehensible way to represent patient document. A retrospective analysis of 1165 discharge notes, from the department Seoul National University Hospital, were extracted by querying OCS(Order Communication System) and taking every discharge note of main disease issued over one year period (2003.01.01~2003.12.31). RESULTS: An XML-based prototype for discharge note has been put into place representing the required "section" and "specific instance". In addition, a subset of the CDA(Clinical Document Architecture) Level One details has been described and integrated. CONCLUSION: Through the introduction of definitions for sections and specific instances, progress in the development of CDA Level Two and Three might be realized. An XML-based prototype was implemented, allowing a special view on XML data to generate this document type.
Electronic Health Records ; Health Level Seven ; Humans ; Retrospective Studies ; Seoul

Objectives: This review article examines international examples of personal health records (PHRs) in advanced countries and discusses the implications of these examples for the establishment and utilization of PHRs in South Korea. Methods: This article synthesized PHR case reports of Organization for Economic Co-operation and Development (OECD) member countries, the Global Digital Health Partnership website on PHRs, and patient portals of individual countries to review the status of PHR services. The concept and significance of PHRs were also discussed with respect to PHR utilization status in European Union and OECD countries. Results: A review of international PHR services showed that the countries shared common points regarding the establishment of Electronic Health Records and national health information infrastructure. In addition, the countries provided services centered on primary healthcare institutions and public hospitals. However, promoting more positive participation and increasing the PHR acceptance rate requires workflow integration, including Electronic Medical Records, the provision of incentives, and the preparation of a supportive legal framework. Conclusions South Korea is also conducting a national-level PHR project. Since the scope of PHRs is extensive and a wide range of PHR services must be connected, an extensive trial-and-error process will be necessary. A long-term strategy should be prepared, and necessary resources should be secured to establish national-level PHRs.

Objectives: This review article examines international examples of personal health records (PHRs) in advanced countries and discusses the implications of these examples for the establishment and utilization of PHRs in South Korea. Methods: This article synthesized PHR case reports of Organization for Economic Co-operation and Development (OECD) member countries, the Global Digital Health Partnership website on PHRs, and patient portals of individual countries to review the status of PHR services. The concept and significance of PHRs were also discussed with respect to PHR utilization status in European Union and OECD countries. Results: A review of international PHR services showed that the countries shared common points regarding the establishment of Electronic Health Records and national health information infrastructure. In addition, the countries provided services centered on primary healthcare institutions and public hospitals. However, promoting more positive participation and increasing the PHR acceptance rate requires workflow integration, including Electronic Medical Records, the provision of incentives, and the preparation of a supportive legal framework. Conclusions South Korea is also conducting a national-level PHR project. Since the scope of PHRs is extensive and a wide range of PHR services must be connected, an extensive trial-and-error process will be necessary. A long-term strategy should be prepared, and necessary resources should be secured to establish national-level PHRs.