• 정보과학회지
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• 제35권7호
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• pp.9-25
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• 2017

In this paper, we surveyed state-of-the-art health and wellness platforms. The motivation of this paper is to review the state-of-the-art health and wellness platforms and their maturity with respect to adoption of latest enabling technologies. The is review is classified into four categories: healthcare systems, AI-assisted healthcare, wellness platforms, and open source health and wellness initiatives. From this comprehensive review, it can be stated that the contemporary healthcare systems are well-adopting wellness due to the concentration shift towards prevention. Thus, the gap between health and wellness is slowly yet carefully entering gray area. Where both the domains can freely invoke each other's services, and supporting enabling technologies. Furthermore, the biomedical researchers and physicians are no longer carrying the myopic views of trusting their knowledge for diagnosis. AI-assisted technologies based on machine learning and big data are influencing today's prognosis with trust and confidence.

• 철학연구
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• 제145권
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• pp.71-90
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• 2018

본 본문은 자기추적기술을 기반으로 하는 정밀의료 시대에 자율성 존중 논의를 본격적으로 하기 전에, 존중의 대상이 되는 자율성 개념, 즉 협의생명의료윤리적 자율성과 관계적 자율성을 검토하는 것이 그 목적이다. 먼저 협의의 생명의료윤리적 자율성(narrow bioethical autonomy)으로 규정되는 탐 비첨(Tom Beauchamp)과 제임스 췰드리스(James Childress)가 제시하는 자율성 논의를 검토를 통해서, 자율성 개념의 범위를 확장할 필요성이 있음을 밝힐 것이다. 그런 다음에 협의의 자율성 보다 확장된 광의 자율성으로써 관계적 자율성이 새로운 상황에서 윤리적 지침을 형성하는 대상으로 적합한 자율성 개념임을 제시하고자 한다.

• 대한의용생체공학회:의공학회지
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• 제44권4호
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• pp.264-274
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• 2023

Purpose: The objective is to extract insights that can contribute to the formulation of harmonized international policies and support measures for innovative medical devices and management systems. This study aims to propose effective strategies for future medical device innovation and healthcare delivery. Results: It investigates technological advancements, regulatory approval systems, insurance policies, and successful commercialization cases in South Korea, the United States, and the European Union. In 2018, the FDA implemented insurance coverage for Software as a Medical Device (SaMD) and recognized insurance coverage for Digital Therapeutics (DTx). Germany is a country that ensures permanent reimbursement for healthcare applications since 2020, making it the first country to provide legal health insurance coverage for fostering a digital ecosystem. Conclusion: The findings of this research highlight the importance of cultivating a supportive regulatory and environmental framework to facilitate the adoption of innovative medical devices. Continuous support for research and development (R&D) efforts by companies, along with the validation of clinical effectiveness, is crucial.

• 대한치과의사협회지
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• 제57권11호
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• pp.644-653
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• 2019

Since 2013, adults aged over 20 can receive national health insurance scaling once a year in South Korea. In this study, we analyzed the usage status of national health insurance care service for periodontal disease in 2010-2018 by using Healthcare big data of the Health Insurance Review and Assessment Service. The increase rate of the dental care users was very high at 7.8 and 11.2% in 2013 and 2014, respectively. These are higher than the increase rate of all medical institution users, which is between -1.7 and 3.7%. In 2017, the rate of dental use was 44.4%, which has increased more than 10% compared to 2012. Percent receiver of national health insurance scaling was 19.5% in 2017. The 20s had the highest rate of 23.2%. The rate decreased with age. Based on these results, it can be evaluated that the expansion of national health insurance coverage for scaling improves accessibility to dental care. A more long-term assessment of the effect of periodic dental examination and scaling on reducing the prevalence of periodontal disease is needed. National health insurance coverage should be extended to oral hygiene education and supportive periodontal therapy in order to prevent periodontal disease.

• 한국병원경영학회지
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• 제27권1호
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• pp.1-10
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• 2022

Background: This study aims to empirically compare and evaluate the current status of medical accessibility and health inequality between people with disabilities and without. We calculated the ACSC hospitalization rate, which is a medical accessibility index, for hypertension, a major risk factor for cardiovascular disease that accounts for more than 20% of deaths among people with disabilities using the 2016 National Health Insurance Big Data. Methods: The subjects of the study were a total of 601,520, including 64,018 people with disabilities and 537,501 people without. Logistic regression was performed to analyze the differences in hypertension hospitalization rates adjusted for demographic and sociological characteristics and disease characteristics using SAS 9.4 program. Results: Before adjusting for the characteristics, the hypertension hospitalization rate of people with disabilities was 1.55%, and the people without disabilities were 0.49%. After adjusting, it was found that people with disabilities were 2.11 times higher than people without disabilities, and it was statistically significant. Conclusion: The preventable hospitalization rate of people with disabilities is higher than that of people without, suggesting that the disabled have problems with access to medical care and health inequality. Therefore, the government's policy improvement is required to close the medical gap for the disabled.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• International Journal of Contents
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• 제17권4호
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• Journal of information and communication convergence engineering
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• 제22권1호
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• pp.7-13
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• 2024

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권2호
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• pp.95-106
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• 2016

생체 데이터는 사람에 따라 다르게 나타날 수 있고 사상의학과 밀접한 관계를 가지고 있다. 생체 데이터는 사람의 맥박과 혈압, 심박동 수와 과거의 병력, 노화의 정도, 체질량 지수 등을 의미하며, 이 생체 데이터는 사람의 건강상태를 판별하기 위한 기준 척도로 활용된다. 그렇기 때문에 생체 데이터는 사용하고자 하는 목적에 맞도록 가공되어야 한다. 기존 연구에서는 실시간으로 변화되고 있는 생체 데이터를 현재 시점의 스냅셧으로만 적용하고 있기 때문에 시간의 연속성이 배제되어 있다. 따라서 이 문제를 해결하기 위하여 본 논문에서는 생체 데이터들로 구성되는 Big Data 환경에서 시간의 연속성을 포함하는 생체데이터의 패턴분석 모델을 제안한다. 제안 모델은 치료와 건강증진을 위해 전자침을 사용할 때 침자리의 선정을 신중하게 결정하는데 도움을 줄 수 있다.