• Title/Summary/Keyword: 보건의료빅데이터

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A Study on the Development Issues of Digital Health Care Medical Information (디지털 헬스케어 의료정보의 발전과제에 관한 연구)

  • Moon, Yong
    • Industry Promotion Research
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    • v.7 no.3
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    • pp.17-26
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    • 2022
  • As the well-being mindset to keep our minds and bodies free and healthy more than anything else in the society we live in is spreading, the meaning of health care has become a key part of the 4th industrial revolution such as big data, IoT, AI, and block chain. The advancement of the advanced medical information service industry is being promoted by utilizing convergence technology. In digital healthcare, the development of intelligent information technology such as artificial intelligence, big data, and cloud is being promoted as a digital transformation of the traditional medical and healthcare industry. In addition, due to rapid development in the convergence of science and technology environment, various issues such as health, medical care, welfare, etc., have been gradually expanded due to social change. Therefore, in this study, first, the general meaning and current status of digital health care medical information is examined, and then, developmental tasks to activate digital health care medical information are analyzed and reviewed. The purpose of this article is to improve usability to fully pursue our human freedom.

Canonical correlation between body information and lipid-profile: A study on the National Health Insurance Big Data in Korea

  • Jo, Han-Gue;Kang, Young-Heung
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.1
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    • pp.201-208
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    • 2021
  • This study aims to provide the relevant basis upon which prediction of dyslipidemia should be made based on body information. Using the National Health Insurance big data (3,312,971 people) canonical correlation analysis was performed between body information and lipid-profile. Body information included age, height, weight and waist circumference, while the lipid-profile included total cholesterol, triglycerides, HDL cholesterol and LDL cholesterol. As a result, when the waist circumference and the weight are large, triglycerides increase and HDL cholesterol level decreases. In terms of age, weight, waist circumference, and HDL cholesterol, the canonical variates (the degree of influence) were significantly different according to sex. In particular, the canonical variate was dramatically changed around the forties and fifties in women in terms of weight, waist circumference, and HDL cholesterol. The canonical correlation results of the health care big data presented in this study will help construct a predictive model that can evaluate an individual's health status based on body information that can be easily measured in a non-invasive manner.

Review of the Development and Application of Disease Network (보건행정 연구자를 위한 질병 네트워크의 구축과 응용 고찰)

  • Kyungmin Lee;Ji-Woong Nam;Yewon Jung;Tae Sic Lee;Ki-Bong Yoo
    • Health Policy and Management
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    • v.34 no.3
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    • pp.226-237
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    • 2024
  • This paper reviewed on understanding the disease network model which represents the relationships, such as risks, pathways, and progression trajectories, among various diseases. By utilizing the disease network models, it visualized the trajectories paths of diseases over time and captured potential relationships between diseases that were previously undiscovered, thereby providing novel insights. This study introduced research cases of disease networks using various domestic and international healthcare data based on graph theory and network models, reviewed the methodologies and applications for constructing disease networks, and suggested the potential for their application in health insurance big data. The paper also discussed the limitations of disease network research and proposed future research directions.

Analysis of the propensity of medical expenses for auto insurance patients by type of medical institution (의료기관 종류별 자동차보험 환자의 진료비 성향 분석)

  • Ha, Au-Hyun
    • Journal of Convergence for Information Technology
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    • v.12 no.2
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    • pp.184-191
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    • 2022
  • This study aims to provide basic information necessary to find an efficient management plan for patients using auto insurance. The analysis was conducted on the five-year auto insurance medical expenses review data registered in the health care bigdata Hub from 2016 to 2020. As a result of the analysis, the number one composition ratio of auto insurance inpatient treatment expenses was treatment and surgery fees for Certified tertiary hospitals, hospitalization fees for general hospitals, hospitals and clinics, and treatment and surgery fees for oriental medical institutions and dental hospitals. outpatient treatment expenses was doctor's fee for medical institution, treatment and surgery fees for oriental medical institutions and dental hospitals. The ratio of medication, anesthesia, and special equipment significantly affected the cost of inpatient. And the ratio of physical therapy significantly affected the cost of outpatient.

The Design and Implementation of Smart Clinic Reservation System Using AIoT (AIoT를 이용한 스마트 진료실 예약 시스템의 설계 및 구현)

  • Jun-Hyeog Choi;Key-Won Kim;Myung-Sook Park
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2024.01a
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    • pp.199-201
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    • 2024
  • 최근 병원에서는 빅데이터, 지능형 사물인터넷(AIoT) 등 인공지능 기반 기술들을 활용하여 환자 진료 및 치료 영역은 물론 의료산업 및 의료 시설 등과 관련된 다양한 영역에서의 활용방안을 모색하고 있다. 지능형 사물인터넷(AIoT, Internet of Things)은 AI와 IoT의 기술적인 결합으로 산업의 혁신을 가져와 국가 전체의 생산성을 높일 수 있을 뿐만 아니라 삶의 질의 변화는 물론 병원의 의료 환경에 있어서도 많은 파급 효과를 가져다 줄 것으로 예상하고 있다. 본 논문에서는 병원의 효율적인 공간관리를 위한 AIoT 기반의 가변 스마트 진료실 예약 시스템에 대한 설계 및 구현을 통하여 병원의 주요 자산인 공간이라는 개념을 효율적으로 이용하고 병원 내 소통과 협업을 위한 유연한 진료 환경을 제공함으로서 병원의 규모와 진료 전문성에 맞추어진 가변적 공간 기능을 통해 병원의 경쟁력을 높이는 것을 그 목적으로 하고 있다.

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Performance Evaluation of Medical Big Data Analysis based on RHadoop (RHadoop 기반 보건의료 빅데이터 분석의 성능 평가)

  • Ryu, Woo-Seok
    • The Journal of the Korea institute of electronic communication sciences
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    • v.13 no.1
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    • pp.207-212
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    • 2018
  • As a data analysis tool which is becoming popular in the Big Data era, R is rapidly expanding its user range by providing powerful statistical analysis and data visualization functions. Major advantage of R is its functional scalability based on open source, but its scale scalability is limited, resulting in performance degrades in large data processing. RHadoop, one of the extension packages to complement it, can improve data analysis performance as it supports Hadoop platform-based distributed processing of programs written in R. In this paper, we evaluate the validity of RHadoop by evaluating the performance improvement of RHadoop in real medical big data analysis. Performance evaluation of the analysis of the medical history information, which is provided by National Health Insurance Service, using R and RHadoop shows that RHadoop cluster composed of 8 data nodes can improve performance up to 8 times compared with R.

A Study on Health Care Utilization Rates by Gender and Age: Focusing on Data from the 17th Wave of Korea Welfare Panel (2023) (성별 및 연령대별 보건의료 이용율에 관한 연구 : 한국복지패널 17차 웨이브(2023) 자료를 중심으로)

  • Ok-Yul Yang
    • Journal of the Health Care and Life Science
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    • v.11 no.1
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    • pp.105-114
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    • 2023
  • This study aims to analyze the secondary data of disease distribution and medical service behavior according to gender and income by using the 17th wave data of the Korea Welfare Panel, which is being distributed in April 2023. Data for 7,865 people of raw data generated using the R language were collected, and among them, missing values (NA, - 2,012) were analyzed for 5,853 people. For analysis, average income by health status and gender, relationship with chronic diseases, outpatient visits to medical institutions by gender/age group, type of medical institution used by age group, and annual health checkup usage rate by gender/age group were examined. Through this, the medical utilization rate was higher in men than in women, and the utilization rate of hospitals and clinics was high.

Big Data-based Medical Clinical Results Analysis (빅데이터 기반 의료 임상 결과 분석)

  • Hwang, Seung-Yeon;Park, Ji-Hun;Youn, Ha-Young;Kwak, Kwang-Jin;Park, Jeong-Min;Kim, Jeong-Joon
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.19 no.1
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    • pp.187-195
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    • 2019
  • Recently, it has become possible to collect, store, process, and analyze data generated in various fields by the development of the technology related to the big data. These big data technologies are used for clinical results analysis and the optimization of clinical trial design will reduce the costs associated with health care. Therefore, in this paper, we are going to analyze clinical results and present guidelines that can reduce the period and cost of clinical trials. First, we use Sqoop to collect clinical results data from relational databases and store in HDFS, and use Hive, a processing tool based on Hadoop, to process data. Finally we use R, a big data analysis tool that is widely used in various fields such as public sector or business, to analyze associations.

A Keyword Network Analysis of Standard Medical Terminology for Musculoskeletal System Using Big Data (빅데이터를 활용한 근골격계 표준의료용어에 대한 키워드 네트워크 분석)

  • Choi, Byung-Kwan;Choi, Eun-A;Nam, Moon-Hee
    • Journal of Digital Convergence
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    • v.20 no.5
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    • pp.681-693
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    • 2022
  • The purpose of this study is to suggest a plan to utilize atypical data in the health care field by inferring standard medical terms related to the musculoskeletal system through keyword network analysis of medical records of patients hospitalized for musculoskeletal disorders. The analysis target was 145 summaries of discharge with musculoskeletal disorders from 2015 to 2019, and was analyzed using TEXTOM, a big data analysis solution developed by The IMC. The 177 musculoskeletal related terms derived through the primary and secondary refining processes were finally analyzed. As a result of the study, the frequent term was 'Metastasis', the clinical findings were 'Metastasis', the symptoms were 'Weakness', the diagnosis was 'Hepatitis', the treatment was 'Remove', and the body structure was 'Spine' in the analysis results for each medical terminology system. 'Oxycodone' was used the most. Based on these results, we would like to suggest implications for the analysis, utilization, and management of unstructured medical data.

Comparison of Scala and R for Machine Learning in Spark (스파크에서 스칼라와 R을 이용한 머신러닝의 비교)

  • Woo-Seok Ryu
    • The Journal of the Korea institute of electronic communication sciences
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    • v.18 no.1
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    • pp.85-90
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    • 2023
  • Data analysis methodology in the healthcare field is shifting from traditional statistics-oriented research methods to predictive research using machine learning. In this study, we survey various machine learning tools, and compare several programming models, which utilize R and Spark, for applying R, a statistical tool widely used in the health care field, to machine learning. In addition, we compare the performance of linear regression model using scala, which is the basic languages of Spark and R. As a result of the experiment, the learning execution time when using SparkR increased by 10 to 20% compared to Scala. Considering the presented performance degradation, SparkR's distributed processing was confirmed as useful in R as the traditional statistical analysis tool that could be used as it is.