Automatic Classification of Thyroid Findings Using Static and Contextualized Ensemble Natural Language Processing Systems: Development Study.

Background: In the case of Korean institutions and enterprises that collect nonstandardized and nonunified formats of electronic medical examination results from multiple medical institutions, a group of experienced nurses who can understand the results and related contexts initially classified the reports manually. The classification guidelines were established by years of workers' clinical experiences and there were attempts to automate the classification work. However, there have been problems in which rule-based algorithms or human labor-intensive efforts can be time-consuming or limited owing to high potential errors.

Deep-learning-based automated terminology mapping in OMOP-CDM.

Objective: Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision. This study aims to construct a trainable system that can automate the process of semantic interinstitutional code mapping.

Understanding Time Series Patterns of Weight and Meal History Reports in Mobile Weight Loss Intervention Programs: Data-Driven Analysis.

Background: Mobile apps for weight loss provide users with convenient features for recording lifestyle and health indicators; they have been widely used for weight loss recently. Previous studies in this field generally focused on the relationship between the cumulative nature of self-reported data and the results in weight loss at the end of the diet period. Therefore, we conducted an in-depth study to explore the relationships between adherence to self-reporting and weight loss outcomes during the weight reduction process.

Enchanted Life Space: Adding Value to Smart Health by Integrating Human Desires.

Objectives: Developments in advanced technology have unlocked an era of smart health, transforming healthcare practices inside and outside hospitals for both medical staff and patients. It is now possible for patients to collect detailed health data using smartphones and wearable devices, regardless of their physical location or time zone. The use of these patient-generated data holds great promise for future healthcare advancements in many ways; however, current strategies for smart-health technologies tend to focus on the smartness of the technology itself and on managing a particular disease or condition.

Learning representations for the early detection of sepsis with deep neural networks.

Background: Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens.

Objective: This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction.

Differences among admitting departments in alerts and alert overrides for drug-drug interaction.

Objective: To determine differences in the incidence and risk factors of alerts for drug-drug interaction (DDI) and the rate of alert overrides by an admitting department.

Methods: A retrospective cohort study was performed using electronic health records of a Korean tertiary teaching hospital including all hospitalized adult patients for 18 months. The main outcome measures included incidence rates of alerts for DDI and their override, hazard ratios (HRs) for DDI alerts, and odds ratios (ORs) for alert overrides by admitting department (emergency department [ED], general ward [GW], and intensive care unit [ICU]) after adjusting for other known risk factors.

Prediction of 4-year risk for coronary artery calcification using ensemble-based classification.

The progression of coronary artery calcification (CAC) has been regarded as an important risk factor of coronary artery disease (CAD), which is the biggest cause of death. Because CAC occurrence increases the risk of CAD by a factor of ten, the one whose coronary artery is calcified should pay more attention to the health management. However, performing the computerized tomography (CT) scan to check if coronary artery is calcified as a regular examination might be inefficient due to its high cost.

Identifying relatively high-risk group of coronary artery calcification based on progression rate: statistical and machine learning methods.

Coronary artery calcification (CAC) score is an important predictor of coronary artery disease (CAD), which is the primary cause of death in advanced countries. Early prediction of high-risk of CAC based on progression rate enables people to prevent CAD from developing into severe symptoms and diseases. In this study, we developed various classifiers to identify patients in high risk of CAC using statistical and machine learning methods, and compared them with performance accuracy.

Integration of heterogeneous clinical decision support systems and their knowledge sets: feasibility study with Drug-Drug Interaction alerts.

There exist limitations in both commercial and in-house clinical decision support systems (CDSSs) and issues related to the integration of different knowledge sources and CDSSs. We chose Standard-based Shareable Active Guideline Environment (SAGE) as a new architecture with knowledge integration and a centralized knowledge base which includes authoring/management functions and independent CDSS, and applied it to Drug-Drug Interaction (DDI) CDSS. The aim of this study was to evaluate the feasibility of the newly integrated DDI alerting CDSS into a real world hospital information system involving construction of an integrated CDSS derived from two heterogeneous systems and their knowledge sets.

High-Resolution Actigraphic Analysis of ADHD: A Wide Range of Movement Variability Observation in Three School Courses - A Pilot Study.

Objectives: This study was conducted to determine whether or not newly proposed high-resolution activity features could provide a superior analytic foundation compared to those commonly used to assess transitions in children's activities, under circumstances in which the types of courses attended exert different situational effects on activity levels.

Methods: From 153 children at a local elementary school, 10 subjects with attention deficit hyperactivity disorder (ADHD) and 7 controls were recruited. Their activity data was collected using an actigraph while they attended school.

Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis.

Objectives: To develop and evaluate time series models to predict the daily number of patients visiting the Emergency Department (ED) of a Korean hospital.

Methods: Data were collected from the hospital information system database. In order to develop a forecasting model, we used, 2 years of data from January 2007 to December 2008 data for the following 3 consecutive months were processed for validation.

A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database.

Purpose: Quantitative analytic methods are being increasingly used in postmarketing surveillance. However, currently existing methods are limited to spontaneous reporting data and are inapplicable to hospital electronic medical record (EMR) data. The principal objectives of this study were to propose a novel algorithm for detecting the signals of adverse drug reactions using EMR data focused on laboratory abnormalities after treatment with medication, and to evaluate the potential use of this method as a signal detection tool.

Toward an objective indexing system for ADHD-screening using children's activity monitoring.

Signs of ADHD are discernible in specific situations, and usually assessed according to subjective impressions. We performed a preliminary comparative study from children's activity at a natural classroom environment with 3-axis accelerator for a feasible objective index. From a total of 157 children (7-9 yrs) and clinically diagnosed 24 children out of them, variances in 1-min epoch mean activity had shown significant differences among the subgroups: (1) ADHD=.