Comparison of mortality prediction models for road traffic accidents: an ensemble technique for imbalanced data.

Background: Injuries caused by RTA are classified under the International Classification of Diseases-10 as 'S00-T99' and represent imbalanced samples with a mortality rate of only 1.2% among all RTA victims. To predict the characteristics of external causes of road traffic accident (RTA) injuries and mortality, we compared performances based on differences in the correction and classification techniques for imbalanced samples.

Descriptive epidemiology of cancer of unknown primary in South Korea, 1999-2017.

Background: Cancers of unknown primary (CUPs) are tumors found after metastasizing from unidentified primary sites; these tumors generally have unknown treatment strategies, expected treatment results, and prognosis. We assessed the epidemiological characteristics of CUPs in Korea.

Methods: We extracted records for 1999 through 2017 from the Korea Central Cancer Registry using the International Statistical Classification of Diseases and Related Health Problems (10th revision) codes for CUP as defined by the International Agency for Research on Cancer.

Comparison of Prediction Models for Mortality Related to Injuries from Road Traffic Accidents after Correcting for Undersampling.

In this study, four models-logistic regression (LR), random forest (RF), linear support vector machine (SVM), and radial basis function (RBF)-SVM-were compared for their accuracy in determining mortality caused by road traffic injuries. They were tested using five years of national-level data from the Korea Disease Control and Prevention Agency's (KDCA) National Hospital Discharge In-Depth Survey (2013 through to 2017). Model performance was measured for accuracy, precision, recall, F1 score, and Brier score metrics using classification analysis that included characteristics of patients, accidents, injuries, and illnesses.

Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality.

(1) Medical research has shown an increasing interest in machine learning, permitting massive multivariate data analysis. Thus, we developed drug intoxication mortality prediction models, and compared machine learning models and traditional logistic regression. (2) Categorized as drug intoxication, 8,937 samples were extracted from the Korea Centers for Disease Control and Prevention (2008-2017).

Survey of Potential Cancer Registry Items to Enhance the Usefulness of Cancer Registry Data.

Background: Cancer registry data can help plan for cancer services and to identify where further progress is needed, in order to improve the lives of patients with cancer. This study investigated the possibility of collecting additional information and the priority of the information by examining other cancer registry items. We aimed to suggest additional data items to be collected to enhance the usefulness of cancer registry data.

Qualitative Improvement Methods Through Analysis of Inquiry Contents for Cancer Registration.

Background: In Korea, the national cancer database was constructed after the initiation of the national cancer registration project in 1980, and the annual national cancer registration report has been published every year since 2005. Consequently, data management must begin even at the stage of data collection in order to ensure quality. Objectives: To determine the suitability of cancer registries’ inquiry tools through the inquiry analysis of the Korea Central Cancer Registry (KCCR), and identify the needs to improve the quality of cancer registration.

Cancer Registration Manual Understanding by Medical Record Administrators.

Background: This study examined: (a) whether medical record administrators (MRAs) properly understand and apply a cancer registration manual; (b) weaknesses in cancer registration: and (c) factors influencing the accuracy of cancer registration. Methods: MRAs dealing with cancer registration in 81 national member hospitals were asked by mail to answer 17 questions about cancer information among registration items. After calculating rates of correct and incorrect answers to each question, an analysis was conducted of whether they were related to particular factors (hospital type, hospital location, number of hospital beds, experience of cancer registration, and experience of consulting cancer registration).

Stakeholder analysis for adopting a personal health record standard in Korea.

Background: Interest in health information exchanges (HIEs) is increasing. Several countries have adopted core health data standards with appropriate strategies.

Objective: This study was conducted to determine the feasibility of a continuity of care record (CCR) as the standard for an electronic version of the official transfer note and the HIE in Korean healthcare.

Improving accuracy and completeness in the collaborative staging system for stomach cancer in South Korea.

Background: Cancer staging enables planning for the best treatments, evaluation of prognosis, and predictions for survival. The Collaborative Stage (CS) system makes it possible to significantly reduce the proportion of patients labeled at an "unknown" stage as well as discrepancies among different staging systems. This study aims to analyze the factors that influence the accuracy and validity of CS data.

Implementation plans for the Korean certified tumor registrar qualification system.

Background: Cancer registration data is used to understand the nation's cancer burden, and to provide significant baseline data for cancer control efforts, as well as, research on cancer incidence, mortality, survival, and prevalence. A system that approves, assesses, and manages the qualification of specialists, responsible for performing cancer registration, has not been developed in Korea. This study presents ways to implement a certification system designed for the qualification of tumor registrars in Korea.

Analysis of questions regarding morbidity coding posted to the online coding clinic of the Korean Medical Record Association.

Accuracy and consistency in morbidity coding are important in both clinical research and practice. However,Health Information Managers (HIMs) sometimes face difficulties in assigning morbidity codes. To assist them,the Korean Medical Record Association operates an online coding clinic bulletin board, on which HIMs can post questions and receive answers.

A study of the difference in volume of information in chief complaint and present illness between electronic and paper medical records.

The introduction of an electronic medical record (EMR) has been rapidly accelerating in South Korea. The EMR was expected to improve quality of care, readability, availability, and the quality of data. However, the reluctance of healthcare providers to use the EMR may have caused a reduction of information recorded in EMRs.

A development of conceptual framework for structured data entry in procedure.

This study was performed to provide the well-designed conceptual framework of clinical contents in procedures that enables clinicians to execute structured data entry at the point of care. The conceptual framework was developed through applying the ISO's conceptual framework for clinical contents modeling, semantic structuring, and using the Entity-Attribute-Value (EAV) model for data representation. We provided the conceptual framework in procedures, including twenty-one attributes and structured clinical data sets for operation records that are six diseases based on the conceptual framework.