Differences in the heritability of craniofacial skeletal and dental characteristics between twin pairs with skeletal Class I and II malocclusions / 대한치과교정학회지

Objective@#To investigate differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and Class II malocclusions. @*Methods@#Forty Korean adult twin pairs were divided into Class I (C-I) group (0° ≤ angle between point A, nasion, and point B [ANB]) ≤ 4°; mean age, 40.7 years) and Class II (C-II) group (ANB > 4°; mean age, 43.0 years). Each group comprised 14 monozygotic and 6 dizygotic twin pairs. Thirty-three cephalometric variables were measured using lateral cephalograms and were categorized as the anteroposterior, vertical, dental, mandible, and cranial base characteristics. The ACE model was used to calculate heritability (A > 0.7, high heritability). Thereafter, principal component analysis (PCA) was performed. @*Results@#Twin pairs in C-I group exhibited high heritability values in the facial anteroposterior characteristics, inclination of the maxillary and mandibular incisors, mandibular body length, and cranial base angles. Twin pairs in C-II group showed high heritability values in vertical facial height, ramus height, effective mandibular length, and cranial base length. PCA extracted eight components with 88.3% in the C-I group and seven components with 91.0% cumulative explanation in the C-II group. @*Conclusions@#Differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and II malocclusions might provide valuable information for growth prediction and treatment planning.

The Improvement and Completion of Outcome index: A new assessment system for quality of orthodontic treatment / 대한치과교정학회지

OBJECTIVE: Given the considerable disagreement between the Peer Assessment Rating (PAR) index and the American Board of Orthodontics Cast-Radiograph Evaluation, we aimed to develop a novel assessment system-the Improvement and Completion of Outcome (ICO) index-to evaluate the outcome of orthodontic treatment. METHODS: Sixteen criteria from 4 major categories were established to represent the pretreatment malocclusion status, as well as the degree of improvement and level of completion of outcome during/after treatment: dental relationship (arch length discrepancy, irregularity, U1-SN, and IMPA); anteroposterior relationship (overjet, right and left molar position, ANB); vertical relationship (anterior overbite, anterior open-bite, lateral open-bite, SN-MP); and transverse relationship (dental midline discrepancy, chin point deviation, posterior cross-bite, occlusal plane cant). The score for each criterion was defined from 0 or −1 (worst) to 5 (ideal value or normal occlusion) in gradations of 1. The sum of the scores in each category indicates the area and extent of the problems. Improvement and completion percentages were estimated based on the pre- and post-treatment total scores and the maximum total score. If the completion percentage exceeded 80%, treatment outcome was considered successful. RESULTS: Two cases, Class I malocclusion and skeletal Class III malocclusion, are presented to represent the assessment procedure using the ICO index. The difference in the level of improvement and completion of treatment outcome can be clearly explained by using 2 percentage values. CONCLUSIONS: Thus, the ICO index enables the evaluation of the quality of orthodontic treatment objectively and consecutively throughout the entire treatment process.

Evaluation of electrical impedance ratio measurements in accuracy of electronic apex locators

OBJECTIVES: The aim of this paper was evaluating the ratios of electrical impedance measurements reported in previous studies through a correlation analysis in order to explicit it as the contributing factor to the accuracy of electronic apex locator (EAL). MATERIALS AND METHODS: The literature regarding electrical property measurements of EALs was screened using Medline and Embase. All data acquired were plotted to identify correlations between impedance and log-scaled frequency. The accuracy of the impedance ratio method used to detect the apical constriction (APC) in most EALs was evaluated using linear ramp function fitting. Changes of impedance ratios for various frequencies were evaluated for a variety of file positions. RESULTS: Among the ten papers selected in the search process, the first-order equations between log-scaled frequency and impedance were in the negative direction. When the model for the ratios was assumed to be a linear ramp function, the ratio values decreased if the file went deeper and the average ratio values of the left and right horizontal zones were significantly different in 8 out of 9 studies. The APC was located within the interval of linear relation between the left and right horizontal zones of the linear ramp model. CONCLUSIONS: Using the ratio method, the APC was located within a linear interval. Therefore, using the impedance ratio between electrical impedance measurements at different frequencies was a robust method for detection of the APC.

Evaluation of electrical impedance ratio measurements in accuracy of electronic apex locators

OBJECTIVES: The aim of this paper was evaluating the ratios of electrical impedance measurements reported in previous studies through a correlation analysis in order to explicit it as the contributing factor to the accuracy of electronic apex locator (EAL). MATERIALS AND METHODS: The literature regarding electrical property measurements of EALs was screened using Medline and Embase. All data acquired were plotted to identify correlations between impedance and log-scaled frequency. The accuracy of the impedance ratio method used to detect the apical constriction (APC) in most EALs was evaluated using linear ramp function fitting. Changes of impedance ratios for various frequencies were evaluated for a variety of file positions. RESULTS: Among the ten papers selected in the search process, the first-order equations between log-scaled frequency and impedance were in the negative direction. When the model for the ratios was assumed to be a linear ramp function, the ratio values decreased if the file went deeper and the average ratio values of the left and right horizontal zones were significantly different in 8 out of 9 studies. The APC was located within the interval of linear relation between the left and right horizontal zones of the linear ramp model. CONCLUSIONS: Using the ratio method, the APC was located within a linear interval. Therefore, using the impedance ratio between electrical impedance measurements at different frequencies was a robust method for detection of the APC.

Prediction Model for Health-Related Quality of Life of Elderly with Chronic Diseases using Machine Learning Techniques / 대한의료정보학회지

OBJECTIVES: The purposes of this study were to identify the factors that affect the health-related quality of life (HRQoL) of the elderly with chronic diseases and to subsequently develop from such factors a prediction model to help identify HRQoL risk groups that require intervention. METHODS: We analyzed a set of secondary data regarding 716 individuals extracted from the Korea National Health and Nutrition Examination Survey from 2008 to 2010. The statistical package of SPSS and MATLAB were used for data analysis and development of the prediction model. The algorithms used in the study were the following: stepwise logistic regression (SLR) analysis and machine learning (ML) techniques, such as decision tree, random forest, and support vector machine methods. RESULTS: Five factors with statistical significance were identified for HRQoL in the elderly with chronic diseases: 'monthly income', 'diagnosis of chronic disease', 'depression', 'discomfort', and 'perceived health status.' The SLR analysis showed the best performance with accuracy = 0.93 and F-score = 0.49. The results of this study provide essential materials that will help formulate personalized health management strategies and develop interventions programs towards the improvement of the HRQoL for elderly people with chronic diseases. CONCLUSIONS: Our study is, to our best knowledge, the first attempt to identify the influencing factors and to apply prediction models for the HRQoL of the elderly with chronic diseases by using ML techniques as an alternative and complement to the traditional statistical approaches.

Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models / 대한의료정보학회지

OBJECTIVES: The aim of this study was to establish a prediction model of medication adherence in elderly patients with chronic diseases and to identify variables showing the highest classification accuracy of medication adherence in elderly patients with chronic diseases using support vector machine (SVM) and conventional statistical methods, such as logistic regression (LR). METHODS: We included 293 chronic disease patients older than 65 years treated at one tertiary hospital. For the medication adherence, Morisky's self-report was used. Data were collected through face-to-face interviews. The mean age of the patients was 73.8 years. The classification process was performed with LR (SPSS ver. 20.0) and SVM (MATLAB ver. 7.12) method. RESULTS: Taking into account 16 variables as predictors, the result of applying LR and SVM classification accuracy was 71.1% and 97.3%, respectively. We listed the top nine variables selected by SVM, and the accuracy using a single variable, self-efficacy, was 72.4%. The results suggest that self-efficacy is a key factor to medication adherence among a Korean elderly population both in LR and SVM. CONCLUSIONS: Medication non-adherence was strongly associated with self-efficacy. Also, modifiable factors such as depression, health literacy, and medication knowledge associated with medication non-adherence were identified. Since SVM builds an optimal classifier to minimize empirical classification errors in discriminating between patient samples, it could achieve a higher accuracy with the smaller number of variables than the number of variables used in LR. Further applications of our approach in areas of complex diseases, treatment will provide uncharted potentials to researchers in the domains.

The Development of Medical Record Items: a User-centered, Bottom-up Approach / 대한의료정보학회지

OBJECTIVES: Clinical documents (CDs) have evolved from traditional paper documents containing narrative text information into the electronic record sheets composed of itemized records, where each record is expressed as an item with a specific value. We defined medical record (MR) items to be information entities with a specific value. These entities were then used to compile form-based clinical documents as part of an electronic health record system (EHR-s). METHODS: We took a reusable bottom-up developmental approach for the MR items, which provided three things: efficient incorporation of the local needs and requirements of the medical professionals from various departments in the hospital, comprehensive inclusion of the essential concepts of the basic elements required in clinical documents, and the provision of a structured means for meaningful data entry and retrieval. This paper delineates our experiences in developing and managing medical records at a large tertiary university hospital in Korea. RESULTS: We collected 63,232 MR items from paper records scanned into 962 CDs. The MR item database was constructed using 13,287 MR items after removing redundant items. During the first year of service users requested changes to be made to 235 (1.8%) attributes of the MR items and also requested the additional 9,572 new MR items. In the second year, the attributes of 70 (0.5%) of the existing MR items were changed and 3,704 new items were added. The number of registered MR items increased by 72.0% in the first year and 27.9% in the second year. CONCLUSIONS: The MR item concept provides an easier and more structured means of data entry within an EHR-s. By using these MR items, various kinds of clinical documents can be easily constructed and allows for medical information to be reused and retrieved as data. The success of the use of MR items in a large tertiary university hospital system provides evidence that verifies our approach as being an efficient means of user-oriented and structured data entry, enabling the easy reuse of medical records.

A Social Network Analysis of Research Topics in Korean Nursing Science

PURPOSE: This study was done to explore the knowledge structure of Korean Nursing Science. METHODS: The main variables were key words from the research papers that were presented in the Journal of Korean Academy of Nursing and journals of the seven branches of the Korean Academy of Nursing. English titles and abstracts of the papers (n=5,936) published from 1995 through 2009 were included. Noun phrases were extracted from the corpora using an in-house program (BiKE Text Analyzer), and their co-occurrence networks were generated via a cosine similarity measure, and then the networks were analyzed and visualized using Pajek, a Social Network Analysis program. RESULTS: With the hub and authority measures, the most important research topics in Korean Nursing Science were identified. Newly emerging topics by three-year period units were observed as research trends. CONCLUSION: This study provides a systematic overview on the knowledge structure of Korean Nursing Science. The Social Network Analysis for this study will be useful for identifying the knowledge structure in Nursing Science.

Application of Support Vector Machine for Prediction of Medication Adherence in Heart Failure Patients / 대한의료정보학회지

OBJECTIVES: Heart failure (HF) is a progressive syndrome that marks the end-stage of heart diseases, and it has a high mortality rate and significant cost burden. In particular, non-adherence of medication in HF patients may result in serious consequences such as hospital readmission and death. This study aims to identify predictors of medication adherence in HF patients. In this work, we applied a Support Vector Machine (SVM), a machine-learning method useful for data classification. METHODS: Data about medication adherence were collected from patients at a university hospital through self-reported questionnaire. The data included 11 variables of 76 patients with HF. Mathematical simulations were conducted in order to develop a SVM model for the identification of variables that would best predict medication adherence. To evaluate the robustness of the estimates made with the SVM models, leave-one-out cross-validation (LOOCV) was conducted on the data set. RESULTS: The two models that best classified medication adherence in the HF patients were: one with five predictors (gender, daily frequency of medication, medication knowledge, New York Heart Association [NYHA] functional class, spouse) and the other with seven predictors (age, education, monthly income, ejection fraction, Mini-Mental Status Examination-Korean [MMSE-K], medication knowledge, NYHA functional class). The highest detection accuracy was 77.63%. CONCLUSIONS: SVM modeling is a promising classification approach for predicting medication adherence in HF patients. This predictive model helps stratify the patients so that evidence-based decisions can be made and patients managed appropriately. Further, this approach should be further explored in other complex diseases using other common variables.

Knowledge Structure of Korean Medical Informatics: A Social Network Analysis of Articles in Journal and Proceedings / 대한의료정보학회지

OBJECTIVES: This study aimed at exploring the knowledge structure of Korean medical informatics. METHODS: We utilized the keywords, as the main variables, of the research papers that were presented in the journal and symposia of the Korean Society of Medical Informatics, and we used, as cases, the English titles and abstracts of the papers (n = 915) published from 1995 through 2008. N-grams (bigram to 5-gram) were extracted from the corpora using the BiKE Text Analyzer, and their cooccurrence networks were generated via a cosine correlation coefficient, and then the networks were analyzed and visualized using Pajek. RESULTS: With the hub and authority measures, the most important research topics in Korean medical informatics were identified. Newly emerging topics by three-year period units were observed as research trends. CONCLUSIONS: This study provides a systematic overview on the knowledge structure of Korean medical informatics.

How to Deal with Medical Narratives based on Controlled Natural Language Operated by an Ontology / 대한의료정보학회지

OBJECTIVE: Medical narratives entry is a major issue to be solved in developing an electronic medical record system operating in practice, as they are, in large part, described in a free-text format. The issue can be dealt with in three aspects: to improve the reusability by structuring medical narratives, to support clinical pragmatics in medical fields, and to reduce the burden of data entry. With the aspects having in mind, this paper purports to present an ontological method for better way of medical narratives entry. METHODS: We developed an ontology for which medical knowledge is structurally represented. Then we can enter medical narrative texts with commands of the controlled natural language operable on the ontology model. RESULTS: Many theoretical studies on free-text entry were reviewed, based on which an authoring and editing tool for natural language description operable on the ontology model has been developed and tested. The performance of the tool is satisfactory within the limit of the domain models we developed here. CONCLUSION: The results of this paper are contributive for clinicians to make an easy entry of medical narratives as far as the ontology model covers their knowledge domain. It is also expected that the cost in recording medical narratives might be considerably reduced and data quality can be improved.

Research and Development Efforts Aimed at Implementing Electronic Health Record Systems with Interoperability and Patient Safety / 대한의료정보학회지

Electronic health records (EHR) hold promise in improving the quality and efficiency of health care, yet the health care system remains years behind other industries with respect to the adoption of information technology (IT). Stakeholders in the medical community, including government and tertiary hospitals, have emphasized the urgent need to adopt IT systems. This paper reviews the current research and development efforts related to EHR in Korea, which have been supported by the government since December 2005, on the basis of the following core EHR components: EHR architectures that incorporate an external, implementation-independent view of a complete EHR; EHR functionality for defining tasks that an EHR system should perform; semantic ontology for developing standard vocabularies at the national level; EHR messaging standards for exchanging data; and clinical decision support systems for improving patient safety. Recent international work on EHR systems and the underlying trends are described, and suitable directions for research and development are suggested under relevant subtopics. Advances in the relevant areas will greatly facilitate our ability to achieve interoperability and promote patient safety. However, EHR systems will perform optimally only if we improve our understanding of the political, structural, and technical foundations for EHR, and reach consensus via collaborations between all the stakeholders in the health care system.

Classification and Conceptualization of Clinical Documents using Formal Concept Analysis / 대한의료정보학회지

OBJECTIVE: Ontology is becoming a core research field in the realm of medical informatics. The objective of our ongoing research is to explore the potential role of Formal Concept Analysis(FCA) in a context-based ontology building support in a medical domain. The concept hierarchy plays an important role as the backbone of ontology, but its construction is a complex and time-consuming process. We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from clinical documents. METHODS: Our approach is based on FCA, a mathematical tool used in data analysis and knowledge engineering. It provides methods to group objects and attributes into concepts, pairs of object-sets(clinical documents) and attribute-sets(fields contained in the clinical documents), such that the binary relation can be presented in a concept lattice. Based on the FCA, we have applied out approach for 8 clinical documents used in a university hospital. As a result of our experiments, we can extract 15 concepts with 7 common fields that can be shared with 8 clinical documents. RESULTS: We show how FCA can be used to classify clinical documents and acquire a concept hierarchy for the medical domain out of the clinical documents with maximal property factorization. CONCLUSION: The whole of our work is based on the concept lattice of which allows to construct a "well defined" ontological concept hierarchy. As an application of this approach, we presented some results of classification of clinical documents with maximally factorized common fields. We have shown that FCA can be useful method to classify and analyze various medical data by constructing concept hierarchy. From that concept hierarchy, we can acquire well-structured facts and knowledges in medical domain.

Ontology Technology in Medical Informatics / 대한의료정보학회지

The main purpose of this paper is to overview the current issues in the area of medical ontology. Ontology technology in Medical Informatics is evolved from the three different research areas: namely, web application for the Semantic Web, Knowledge Representation in Artificial Intelligence, and medical terminology system. In this paper we provide possible research agenda concerning medical ontology development from the above three perspectives at the same time.