• Journal of Environmental Science International
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• v.28 no.7
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• pp.617-627
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• 2019

This study investigated the relationship between heat-related illnesses obtained from healthcare big data and daily maximum temperature observed in seven metropolitan cities in summer during 2013~2015. We found a statistically significant positive correlation (r = 0.4~0.6) between daily maximum temperature and number of the heat-related patients from Pearson's correlation analyses. A time lag effect was not observed. Relative Risk (RR) analysis using the Generalized Additive Model (GAM) showed that the RR of heat-related illness increased with increasing threshold temperature (maximum RR = 1.21). A comparison of the RRs of the seven cities, showed that the values were significantly different by geographical location of the city and had different variations for different threshold temperatures. The RRs for elderly people were clearly higher than those for the all-age group. Especially, a maximum value of 1.83 was calculated at the threshold temperature of $35^{\circ}C$ in Seoul. In addition, relatively higher RRs were found for inland cities (Seoul, Gwangju, Daegu, and Daejeon), which had a high frequency of heat waves. These results demonstrate the significant risk of heat-related illness associated with increasing daily maximum temperature and the difference in adaptation ability to heat wave for each city, which could help improve the heat wave advisory and warning system.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.313-325
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• 2022

The purpose of this study is to examine the changes in the elderly healthcare app market through text mining analysis and to present basic data for activating elderly healthcare apps. Data collection was conducted on Naver, Daum, blog web, and cafe. As for the research method, text mining, TF-IDF(Term frequency-inverse document frequency), emotional analysis, and semantic network analysis were conducted using Textom and Ucinet6, which are big data analysis programs. As a result of this study, a total of six categories were finally derived: resolving the healthcare app information gap, convergence healthcare technology, diffusion media, elderly healthcare app industry, social background, and content. In conclusion, in order for elderly healthcare apps to be accepted and utilized by the elderly, they must have a good diffusion infrastructure, and the effectiveness of healthcare apps must be maximized through the active introduction of convergence technology and content development that can be easily used by the elderly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.560-563
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• 2016

The changes in the medical and healthcare are started from the digital technology. The new field of digital healthcare has started fused with existing healthcare, medical technology, and digital technology. It can increase the service effect and reduce healthcare costs by applying ICT skills such as ICBM(Internet of Things, Cloud, Big data and Mobile), artificial intelligence, robotics, virtual, augmented reality, and wearable devices to healthcare services including healthcare, disease management. Recently there has been grafted an artificial intelligence technologies such as AlphaGo of Google and Watson of IBM onto the healthcare area. In this study, we analyze the main technology, ecosystem, platforms for digital healthcare, and lastly future changes in health care services and issues of digital healthcare.

• Korean Journal of Clinical Pharmacy
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• v.27 no.4
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• pp.221-227
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• 2017

Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.25-37
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• 2022

The government and private companies are endeavoring to help the digital healthcare industry grow. This includes easing regulations on the big data industry such as the amendment of the Data 3 Act. Despite these efforts, however, there have been constant demands for the amendment of laws related to the medical field and for securing medical data transmissions. In this paper, the Data 3 Act of Korea and the legal system related to healthcare are examined. Then the legal, institutional, and technical aspects of the strategies are compared to understand the issues and implications. Based on this, a legal and institutional strategy suitable for the digital healthcare industry in Korea is suggested. Additionally, a direction to improve social perception along with technical measures such as safe de-identification processing and data transmission are also proposed. This study hopes to contribute to the spread of various convergent industries along with the digital healthcare industry.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.203-208
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• 2022

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

• Journal of the Korean Society of Food Culture
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• v.38 no.4
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• pp.185-190
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• 2023

This paper elucidates the novel direction of food research in the era of the 4^th Industrial Revolution characterized by personalized approaches. Since conventional approaches for identifying novel food materials for health benefits are expensive and time-consuming, there is a need to shift towards AI-based approaches which offer more efficient and cost-effective methods, thus accelerating progress in the field of food science. However, relevant research papers in this field present several challenges such as regional and ethnic differences and lack of standardized data. To tackle this problem, our study proposes to address the issues by acquiring and normalizing food and biological big data. In addition, the paper demonstrates the association between heath status and biological big data such as metabolome, epigenome, and microbiome for personalized healthcare. Through the integration of food-health-bio data with AI technologies, we propose solutions for personalized healthcare that are both effective and validated.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.480-481
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• 2018

이 논문은 발목에 만성적인 불안정성을 가진 환자의 발목을 재사용하는 것을 방지하기 위한 것이다. 이를 위해 발목의 안쪽 굽힙과 바깥쪽 굽힘의 각도가 측정된다. 각도가 규정 된 범위 내에 있고 환자가 통증을 호소하지 않으면 정상으로 간주한다. 그러나 각도가 규정 된 범위를 벗어나서 환자가 통증을 호소하는 경우 이상이 있는 것으로 간주되어 이상 증상에 대한 치료가 필요할 것이다.

• Journal of Sensor Science and Technology
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• v.21 no.4
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• pp.241-248
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• 2012

Recently healthcare industry such as pharmaceutical, medical device and healthcare service technology is growing significantly. Mobile healthcare has attracted big attention due to IT convergence technology. Paradigm of healthcare has been changed from the 1st generation(communicable disease prevention) and the 2nd generation(treatment of disease due to extended life expectancy) to the 3rd generation(extended life expectancy due to prevention and control). In our study, we suggest the 3rd generation mobile healthcare system using Bluetooth based wearable ECG monitoring system and smart phone technology. The mobile healthcare system consists of wearable shirts with Bluetooth communication module, ECG sensor, battery, and mobile phone. The ECG data is obtained by a miniaturized sensor and the data is transferred to a mobile phone using Bluetooth communication. Then, user can monitor his/her own ECG signal on an application using Android in mobile phone. The Bluetooth communication device is used due to highly reliable data transmission property and the Bluetooth chip is embedded in every mobile phone. The wearable shirts with chest belt of Bluetooth ECG module is designed with a focus on convenience in the daily life of a wearer. The ECG signal evaluation software in Android based mobile phone is developed for the health check and the ECG signal variation is tested according to the activities of the wearer such as walking, climbing stairs, stand up and sit down, and so on.