PUBLICATIONS
2023
8.
Sunyong Yoo; Ja Young Choi; Shin-seung Yang; Seong-Eun Koh; Myeong-Hyeon Jeong; Min-Keun Song
Abstract | Links | BibTeX | Dimensions | Tags: Medical informatics, National health insurance service
@article{yoo2023medical,
title = {Medical service utilization by children with physical or brain disabilities in South Korea},
author = {Sunyong Yoo and Ja Young Choi and Shin-seung Yang and Seong-Eun Koh and Myeong-Hyeon Jeong and Min-Keun Song},
url = {https://link.springer.com/article/10.1186/s12887-023-04309-2},
doi = {10.1186/s12887-023-04309-2},
year = {2023},
date = {2023-09-26},
urldate = {2023-09-26},
journal = {BMC pediatrics},
volume = {23},
number = {1},
pages = {487},
publisher = {Springer},
abstract = {Background
Children with physical or brain disabilities experience several functional impairments and declining health complications that must be considered for adequate medical support. This study investigated the current medical service utilization of children expressing physical or brain disabilities in South Korea by analyzing medical visits, expenses, and comorbidities.
Methods
We used a database linked to the National Rehabilitation Center of South Korea to extract information on medical services utilized by children with physical or brain disabilities, the number of children with a disability, medical visits for each child, medical expenses per visit, total medical treatment cost, copayments by age group, condition severity, and disability type.
Results
Brain disorder comorbidities significantly differed between those with mild and severe disabilities. Visits per child, total medical treatment cost, and copayments were higher in children with severe physical disabilities; however, medical expenses per visit were lower than those with mild disabilities. These parameters were higher in children with severe brain disabilities than in mild cases. Total medical expenses incurred by newborns to three-year-old children with physical disorders were highest due to increased visits per child. However, medical expenses per visit were highest for children aged 13–18.
Conclusion
Medical service utilization varied by age, condition severity, and disability type. Severe cases and older children with potentially fatal comorbidities required additional economic support. Therefore, a healthcare delivery system for children with disabilities should be established to set affordable medical costs and provide comprehensive medical services based on disability type and severity.},
note = {Correspondence to Min-Keun Song},
keywords = {Medical informatics, National health insurance service},
pubstate = {published},
tppubtype = {article}
}
Background
Children with physical or brain disabilities experience several functional impairments and declining health complications that must be considered for adequate medical support. This study investigated the current medical service utilization of children expressing physical or brain disabilities in South Korea by analyzing medical visits, expenses, and comorbidities.
Methods
We used a database linked to the National Rehabilitation Center of South Korea to extract information on medical services utilized by children with physical or brain disabilities, the number of children with a disability, medical visits for each child, medical expenses per visit, total medical treatment cost, copayments by age group, condition severity, and disability type.
Results
Brain disorder comorbidities significantly differed between those with mild and severe disabilities. Visits per child, total medical treatment cost, and copayments were higher in children with severe physical disabilities; however, medical expenses per visit were lower than those with mild disabilities. These parameters were higher in children with severe brain disabilities than in mild cases. Total medical expenses incurred by newborns to three-year-old children with physical disorders were highest due to increased visits per child. However, medical expenses per visit were highest for children aged 13–18.
Conclusion
Medical service utilization varied by age, condition severity, and disability type. Severe cases and older children with potentially fatal comorbidities required additional economic support. Therefore, a healthcare delivery system for children with disabilities should be established to set affordable medical costs and provide comprehensive medical services based on disability type and severity.
Children with physical or brain disabilities experience several functional impairments and declining health complications that must be considered for adequate medical support. This study investigated the current medical service utilization of children expressing physical or brain disabilities in South Korea by analyzing medical visits, expenses, and comorbidities.
Methods
We used a database linked to the National Rehabilitation Center of South Korea to extract information on medical services utilized by children with physical or brain disabilities, the number of children with a disability, medical visits for each child, medical expenses per visit, total medical treatment cost, copayments by age group, condition severity, and disability type.
Results
Brain disorder comorbidities significantly differed between those with mild and severe disabilities. Visits per child, total medical treatment cost, and copayments were higher in children with severe physical disabilities; however, medical expenses per visit were lower than those with mild disabilities. These parameters were higher in children with severe brain disabilities than in mild cases. Total medical expenses incurred by newborns to three-year-old children with physical disorders were highest due to increased visits per child. However, medical expenses per visit were highest for children aged 13–18.
Conclusion
Medical service utilization varied by age, condition severity, and disability type. Severe cases and older children with potentially fatal comorbidities required additional economic support. Therefore, a healthcare delivery system for children with disabilities should be established to set affordable medical costs and provide comprehensive medical services based on disability type and severity.
2022
7.
Sangyun Lee; Soyeon Lee; Myeonghyeon Jeong; Sunwoo Jung; Myoungjin Lee; Sunyong Yoo
Abstract | Links | BibTeX | Dimensions | Tags: Cataracts, Medical informatics, NHANES, Nutrients, Nutrition surveys
@article{lee2022relationship,
title = {The relationship between nutrient intake and cataracts in the older adult population of Korea},
author = {Sangyun Lee and Soyeon Lee and Myeonghyeon Jeong and Sunwoo Jung and Myoungjin Lee and Sunyong Yoo},
url = {https://www.mdpi.com/2072-6643/14/23/4962},
doi = {10.3390/nu14234962},
year = {2022},
date = {2022-11-23},
urldate = {2022-11-23},
journal = {Nutrients},
volume = {14},
number = {23},
pages = {4962},
publisher = {MDPI},
abstract = {Cataracts are a prevalent ophthalmic disease worldwide, and research on the risk factors for cataracts occurrence is actively being conducted. This study aimed to investigate the relationship between nutrient intake and cataracts in the older adult population in Korea. We analyzed data from Korean adults over the age of 60 years (cataract: 2137, non-cataract: 3497) using the Korean National Health and Nutrition Examination Survey. We performed univariate simple and multiple logistic regressions, adjusting for socio-demographic, medical history, and lifestyle, to identify the associations between nutrient intake and cataracts. A higher intake of vitamin B1 in the male group was associated with a lower incidence of cataracts. A lower intake of polyunsaturated fatty acids and vitamin A, and a higher intake of vitamin B2 in the female group were associated with a higher incidence of cataracts. Our study demonstrated that polyunsaturated fatty acids, vitamin A, and vitamin B2 could affect the incidence of cataracts according to sex. The findings could be used to control nutrient intake for cataract prevention.},
note = {Correspondence to Sunyong Yoo},
keywords = {Cataracts, Medical informatics, NHANES, Nutrients, Nutrition surveys},
pubstate = {published},
tppubtype = {article}
}
Cataracts are a prevalent ophthalmic disease worldwide, and research on the risk factors for cataracts occurrence is actively being conducted. This study aimed to investigate the relationship between nutrient intake and cataracts in the older adult population in Korea. We analyzed data from Korean adults over the age of 60 years (cataract: 2137, non-cataract: 3497) using the Korean National Health and Nutrition Examination Survey. We performed univariate simple and multiple logistic regressions, adjusting for socio-demographic, medical history, and lifestyle, to identify the associations between nutrient intake and cataracts. A higher intake of vitamin B1 in the male group was associated with a lower incidence of cataracts. A lower intake of polyunsaturated fatty acids and vitamin A, and a higher intake of vitamin B2 in the female group were associated with a higher incidence of cataracts. Our study demonstrated that polyunsaturated fatty acids, vitamin A, and vitamin B2 could affect the incidence of cataracts according to sex. The findings could be used to control nutrient intake for cataract prevention.
6.
Seonwoo Jung; Min-Keun Song; Eunjoo Lee; Sejin Bae; Yeon-Yong Kim; Doheon Lee; Myoung Jin Lee; Sunyong Yoo
Abstract | Links | BibTeX | Dimensions | Tags: Atrial fibrillation, Attention mechanism, Deep learning, Machine learning, Medical informatics, National health insurance service, Stroke
@article{jung2022predicting,
title = {Predicting ischemic stroke in patients with atrial fibrillation using machine learning},
author = {Seonwoo Jung and Min-Keun Song and Eunjoo Lee and Sejin Bae and Yeon-Yong Kim and Doheon Lee and Myoung Jin Lee and Sunyong Yoo},
url = {https://www.imrpress.com/journal/FBL/27/3/10.31083/j.fbl2703080/htm?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Frontiers_in_Bioscience-Landmark_TrendMD_1},
doi = {10.31083/j.fbl2703080},
year = {2022},
date = {2022-03-04},
urldate = {2022-03-04},
journal = {Frontiers in Bioscience-Landmark},
volume = {27},
number = {3},
pages = {80},
publisher = {IMR Press},
abstract = {Background
Atrial fibrillation (AF) is a well-known risk factor for stroke. Predicting the risk is important to prevent the first and secondary attacks of cerebrovascular diseases by determining early treatment. This study aimed to predict the ischemic stroke in AF patients based on the massive and complex Korean National Health Insurance (KNHIS) data through a machine learning approach.
Methods
We extracted 65-dimensional features, including demographics, health examination, and medical history information, of 754,949 patients with AF from KNHIS. Logistic regression was used to determine whether the extracted features had a statistically significant association with ischemic stroke occurrence. Then, we constructed the ischemic stroke prediction model using an attention-based deep neural network. The extracted features were used as input, and the occurrence of ischemic stroke after the diagnosis of AF was the output used to train the model.
Results We found 48 features significantly associated with ischemic stroke occurrence through regression analysis (p-value < 0.001). When the proposed deep learning model was applied to 150,989 AF patients, it was confirmed that the occurrence ischemic stroke was predicted to be higher AUROC (AUROC = 0.727 ± 0.003) compared to CHA2DS2-VASc score (AUROC = 0.651 ± 0.007) and other machine learning methods.
Conclusions
As part of preventive medicine, this study could help AF patients prepare for ischemic stroke prevention based on predicted stoke associated features and risk scores.},
note = {Correspondence to Sunyong Yoo},
keywords = {Atrial fibrillation, Attention mechanism, Deep learning, Machine learning, Medical informatics, National health insurance service, Stroke},
pubstate = {published},
tppubtype = {article}
}
Background
Atrial fibrillation (AF) is a well-known risk factor for stroke. Predicting the risk is important to prevent the first and secondary attacks of cerebrovascular diseases by determining early treatment. This study aimed to predict the ischemic stroke in AF patients based on the massive and complex Korean National Health Insurance (KNHIS) data through a machine learning approach.
Methods
We extracted 65-dimensional features, including demographics, health examination, and medical history information, of 754,949 patients with AF from KNHIS. Logistic regression was used to determine whether the extracted features had a statistically significant association with ischemic stroke occurrence. Then, we constructed the ischemic stroke prediction model using an attention-based deep neural network. The extracted features were used as input, and the occurrence of ischemic stroke after the diagnosis of AF was the output used to train the model.
Results We found 48 features significantly associated with ischemic stroke occurrence through regression analysis (p-value < 0.001). When the proposed deep learning model was applied to 150,989 AF patients, it was confirmed that the occurrence ischemic stroke was predicted to be higher AUROC (AUROC = 0.727 ± 0.003) compared to CHA2DS2-VASc score (AUROC = 0.651 ± 0.007) and other machine learning methods.
Conclusions
As part of preventive medicine, this study could help AF patients prepare for ischemic stroke prevention based on predicted stoke associated features and risk scores.
Atrial fibrillation (AF) is a well-known risk factor for stroke. Predicting the risk is important to prevent the first and secondary attacks of cerebrovascular diseases by determining early treatment. This study aimed to predict the ischemic stroke in AF patients based on the massive and complex Korean National Health Insurance (KNHIS) data through a machine learning approach.
Methods
We extracted 65-dimensional features, including demographics, health examination, and medical history information, of 754,949 patients with AF from KNHIS. Logistic regression was used to determine whether the extracted features had a statistically significant association with ischemic stroke occurrence. Then, we constructed the ischemic stroke prediction model using an attention-based deep neural network. The extracted features were used as input, and the occurrence of ischemic stroke after the diagnosis of AF was the output used to train the model.
Results We found 48 features significantly associated with ischemic stroke occurrence through regression analysis (p-value < 0.001). When the proposed deep learning model was applied to 150,989 AF patients, it was confirmed that the occurrence ischemic stroke was predicted to be higher AUROC (AUROC = 0.727 ± 0.003) compared to CHA2DS2-VASc score (AUROC = 0.651 ± 0.007) and other machine learning methods.
Conclusions
As part of preventive medicine, this study could help AF patients prepare for ischemic stroke prevention based on predicted stoke associated features and risk scores.
2021
5.
Hyeonseo Yun; Dong-Wook Kim; Eun-Joo Lee; Jinmyung Jung; Sunyong Yoo
Abstract | Links | BibTeX | Dimensions | Tags: Depression, Dietary habits, Medical informatics, NHANES, Nutrients, Nutrition surveys
@article{yun2021analysis,
title = {Analysis of the effects of nutrient intake and dietary habits on depression in Korean adults},
author = {Hyeonseo Yun and Dong-Wook Kim and Eun-Joo Lee and Jinmyung Jung and Sunyong Yoo},
url = {https://www.mdpi.com/2072-6643/13/4/1360},
doi = {10.3390/nu13041360},
year = {2021},
date = {2021-04-19},
urldate = {2021-04-19},
journal = {Nutrients},
volume = {13},
number = {4},
pages = {1360},
publisher = {MDPI},
abstract = {While several studies have explored nutrient intake and dietary habits associated with depression, few studies have reflected recent trends and demographic factors. Therefore, we examined how nutrient intake and eating habits are associated with depression, according to gender and age. We performed simple and multiple regressions using nationally representative samples of 10,106 subjects from the Korea National Health and Nutrition Examination Survey. The results indicated that cholesterol, dietary fiber, sodium, frequency of breakfast, lunch, dinner, and eating out were significantly associated with depression (p-value < 0.05). Moreover, depression was associated with nutrient intake and dietary habits by gender and age group: sugar, breakfast, lunch, and eating out frequency in the young women’s group; sodium and lunch frequency among middle-age men; dietary fibers, breakfast, and eating out frequency among middle-age women; energy, moisture, carbohydrate, lunch, and dinner frequency in late middle-age men; breakfast and lunch frequency among late middle-age women; vitamin A, carotene, lunch, and eating out frequency among older age men; and fat, saturated fatty acids, omega-3 fatty acid, omega-6 fatty acid, and eating out frequency among the older age women’s group (p-value < 0.05). This study can be used to establish dietary strategies for depression prevention, considering gender and age.},
keywords = {Depression, Dietary habits, Medical informatics, NHANES, Nutrients, Nutrition surveys},
pubstate = {published},
tppubtype = {article}
}
While several studies have explored nutrient intake and dietary habits associated with depression, few studies have reflected recent trends and demographic factors. Therefore, we examined how nutrient intake and eating habits are associated with depression, according to gender and age. We performed simple and multiple regressions using nationally representative samples of 10,106 subjects from the Korea National Health and Nutrition Examination Survey. The results indicated that cholesterol, dietary fiber, sodium, frequency of breakfast, lunch, dinner, and eating out were significantly associated with depression (p-value < 0.05). Moreover, depression was associated with nutrient intake and dietary habits by gender and age group: sugar, breakfast, lunch, and eating out frequency in the young women’s group; sodium and lunch frequency among middle-age men; dietary fibers, breakfast, and eating out frequency among middle-age women; energy, moisture, carbohydrate, lunch, and dinner frequency in late middle-age men; breakfast and lunch frequency among late middle-age women; vitamin A, carotene, lunch, and eating out frequency among older age men; and fat, saturated fatty acids, omega-3 fatty acid, omega-6 fatty acid, and eating out frequency among the older age women’s group (p-value < 0.05). This study can be used to establish dietary strategies for depression prevention, considering gender and age.
4.
정선우; 이민지; 유선용
Abstract | Links | BibTeX | Dimensions | Tags: Machine learning, Medical informatics
@article{정선우2021공공빅데이터를,
title = {공공빅데이터를 활용한 기계학습 기반 뇌졸중 위험도 예측},
author = {정선우 and 이민지 and 유선용},
url = {https://kiss.kstudy.com/Detail/Ar?key=3863715},
doi = {10.12673/jant.2021.25.1.96},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {한국항행학회논문지},
volume = {25},
number = {1},
pages = {96–101},
publisher = {한국항행학회},
abstract = {본 논문은 빅데이터를 이용하여 심방세동 환자의 뇌졸중 발병을 예측하는 기계 학습 모델을 제시한다. 학습 데이터로는 국민 건강 보험공단에서 제공하는 대한민국 전수에 해당하는 심방세동 환자의 정보를 수집하였다. 수집된 정보는 인구사회학, 과거 병력, 건강검진을 포함한 68개 독립변수로 구성된다. 본 연구의 목표는 기존 심방세동 환자의 뇌졸중 위험도 예측에 사용되던 통계적 모델 (CHADS2, CHA2DS2-VASc)의 성능을 검증하고 기계 학습 모델을 적용하여 기존 모델보다 높은 정확도를 가지는 모델을 제시하는 것이다. 제안하는 모델의 정확도, AUROC (area under the receiver operating characteristic)를 검증한 결과 제안하는 기계 학습 기반의 모형이 심방세동 환자의 뇌졸중 위험도를 사용한 모델이 기존의 통계적 모델보다 높은 정확도, 민감도, 특이도를 가지는 것을 확인할 수 있었다.},
keywords = {Machine learning, Medical informatics},
pubstate = {published},
tppubtype = {article}
}
본 논문은 빅데이터를 이용하여 심방세동 환자의 뇌졸중 발병을 예측하는 기계 학습 모델을 제시한다. 학습 데이터로는 국민 건강 보험공단에서 제공하는 대한민국 전수에 해당하는 심방세동 환자의 정보를 수집하였다. 수집된 정보는 인구사회학, 과거 병력, 건강검진을 포함한 68개 독립변수로 구성된다. 본 연구의 목표는 기존 심방세동 환자의 뇌졸중 위험도 예측에 사용되던 통계적 모델 (CHADS2, CHA2DS2-VASc)의 성능을 검증하고 기계 학습 모델을 적용하여 기존 모델보다 높은 정확도를 가지는 모델을 제시하는 것이다. 제안하는 모델의 정확도, AUROC (area under the receiver operating characteristic)를 검증한 결과 제안하는 기계 학습 기반의 모형이 심방세동 환자의 뇌졸중 위험도를 사용한 모델이 기존의 통계적 모델보다 높은 정확도, 민감도, 특이도를 가지는 것을 확인할 수 있었다.
2020
3.
Junseok Park; Seongkuk Park; Kwangmin Kim; Woochang Hwang; Sunyong Yoo; Gwan-su Yi; Doheon Lee
Abstract | Links | BibTeX | Dimensions | Tags: Clinical trial, Medical informatics
@article{park2020interactive,
title = {An interactive retrieval system for clinical trial studies with context-dependent protocol elements},
author = {Junseok Park and Seongkuk Park and Kwangmin Kim and Woochang Hwang and Sunyong Yoo and Gwan-su Yi and Doheon Lee},
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238290},
doi = {10.1371/journal.pone.0238290},
year = {2020},
date = {2020-09-18},
urldate = {2020-09-18},
journal = {PloS one},
volume = {15},
number = {9},
pages = {e0238290},
publisher = {Public Library of Science San Francisco, CA USA},
abstract = {A well-defined protocol for a clinical trial guarantees a successful outcome report. When designing the protocol, most researchers refer to electronic databases and extract protocol elements using a keyword search. However, state-of-the-art database systems only offer text-based searches for user-entered keywords. In this study, we present a database system with a context-dependent and protocol-element-selection function for successfully designing a clinical trial protocol. To do this, we first introduce a database for a protocol retrieval system constructed from individual protocol data extracted from 184,634 clinical trials and 13,210 frame structures of clinical trial protocols. The database contains a variety of semantic information that allows the filtering of protocols during the search operation. Based on the database, we developed a web application called the clinical trial protocol database system (CLIPS; available at https://corus.kaist.edu/clips). This system enables an interactive search by utilizing protocol elements. To enable an interactive search for combinations of protocol elements, CLIPS provides optional next element selection according to the previous element in the form of a connected tree. The validation results show that our method achieves better performance than that of existing databases in predicting phenotypic features.},
keywords = {Clinical trial, Medical informatics},
pubstate = {published},
tppubtype = {article}
}
A well-defined protocol for a clinical trial guarantees a successful outcome report. When designing the protocol, most researchers refer to electronic databases and extract protocol elements using a keyword search. However, state-of-the-art database systems only offer text-based searches for user-entered keywords. In this study, we present a database system with a context-dependent and protocol-element-selection function for successfully designing a clinical trial protocol. To do this, we first introduce a database for a protocol retrieval system constructed from individual protocol data extracted from 184,634 clinical trials and 13,210 frame structures of clinical trial protocols. The database contains a variety of semantic information that allows the filtering of protocols during the search operation. Based on the database, we developed a web application called the clinical trial protocol database system (CLIPS; available at https://corus.kaist.edu/clips). This system enables an interactive search by utilizing protocol elements. To enable an interactive search for combinations of protocol elements, CLIPS provides optional next element selection according to the previous element in the form of a connected tree. The validation results show that our method achieves better performance than that of existing databases in predicting phenotypic features.
2.
Junseok Park; Seongkuk Park; Gwangmin Kim; Kwangmin Kim; Jaegyun Jung; Sunyong Yoo; Gwan-Su Yi; Doheon Lee
Abstract | Links | BibTeX | Dimensions | Tags: Clinical trial, Medical informatics
@article{park2020reliable,
title = {Reliable data collection in participatory trials to assess digital healthcare applications},
author = {Junseok Park and Seongkuk Park and Gwangmin Kim and Kwangmin Kim and Jaegyun Jung and Sunyong Yoo and Gwan-Su Yi and Doheon Lee},
url = {https://ieeexplore.ieee.org/abstract/document/9054970},
doi = {10.1109/ACCESS.2020.2985122},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {IEEE Access},
volume = {8},
pages = {79472–79490},
publisher = {IEEE},
abstract = {The number of digital healthcare mobile applications in the market is exponentially increasing owing to the development of mobile networks and widespread usage of smartphones. However, only few of these applications have been adequately validated. Like many mobile applications, in general, the use of healthcare applications is considered safe; thus, developers and end users can easily exchange them in the marketplace. However, existing platforms are unsuitable for collecting reliable data for evaluating the effectiveness of the applications. Moreover, these platforms reflect only the perspectives of developers and experts, and not of end users. For instance, typical clinical trial data collection methods are not appropriate for participant-driven assessment of healthcare applications because of their complexity and high cost. Thus, we identified the need for a participant-driven data collection platform for end users that is interpretable, systematic, and sustainable, as a first step to validate the effectiveness of the applications. To collect reliable data in the participatory trial format, we defined distinct stages for data preparation, storage, and sharing. The interpretable data preparation consists of a protocol database system and semantic feature retrieval method that allow a person without professional knowledge to create a protocol. The systematic data storage stage includes calculation of the collected data reliability weight. For sustainable data collection, we integrated a weight method and a future reward distribution function. We validated the methods through statistical tests involving 718 human participants. The results of a validation experiment demonstrate that the compared methods differ significantly and prove that the choice of an appropriate method is essential for reliable data collection, to facilitate effectiveness validation of digital healthcare applications. Furthermore, we created a Web-based system for our pilot platform to collect reliable data in an integrated pipeline. We compared the platform features using existing clinical and pragmatic trial data collection platforms.},
keywords = {Clinical trial, Medical informatics},
pubstate = {published},
tppubtype = {article}
}
The number of digital healthcare mobile applications in the market is exponentially increasing owing to the development of mobile networks and widespread usage of smartphones. However, only few of these applications have been adequately validated. Like many mobile applications, in general, the use of healthcare applications is considered safe; thus, developers and end users can easily exchange them in the marketplace. However, existing platforms are unsuitable for collecting reliable data for evaluating the effectiveness of the applications. Moreover, these platforms reflect only the perspectives of developers and experts, and not of end users. For instance, typical clinical trial data collection methods are not appropriate for participant-driven assessment of healthcare applications because of their complexity and high cost. Thus, we identified the need for a participant-driven data collection platform for end users that is interpretable, systematic, and sustainable, as a first step to validate the effectiveness of the applications. To collect reliable data in the participatory trial format, we defined distinct stages for data preparation, storage, and sharing. The interpretable data preparation consists of a protocol database system and semantic feature retrieval method that allow a person without professional knowledge to create a protocol. The systematic data storage stage includes calculation of the collected data reliability weight. For sustainable data collection, we integrated a weight method and a future reward distribution function. We validated the methods through statistical tests involving 718 human participants. The results of a validation experiment demonstrate that the compared methods differ significantly and prove that the choice of an appropriate method is essential for reliable data collection, to facilitate effectiveness validation of digital healthcare applications. Furthermore, we created a Web-based system for our pilot platform to collect reliable data in an integrated pipeline. We compared the platform features using existing clinical and pragmatic trial data collection platforms.
2012
1.
Sunyong Yoo; Moonshik Shin; Doheon Lee; others
Abstract | Links | BibTeX | Dimensions | Tags: k-anonymity, l-diversity, Medical informatics
@article{yoo2012approach,
title = {An approach to reducing information loss and achieving diversity of sensitive attributes in k-anonymity methods},
author = {Sunyong Yoo and Moonshik Shin and Doheon Lee and others},
url = {https://www.i-jmr.org/2012/2/e14},
doi = {10.2196/ijmr.2140},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {Interactive Journal of Medical Research},
volume = {1},
number = {2},
pages = {e2140},
publisher = {JMIR Publications Inc., Toronto, Canada},
abstract = {Electronic Health Records (EHRs) enable the sharing of patients’ medical data. Since EHRs include patients’ private data, access by researchers is restricted. Therefore k-anonymity is necessary to keep patients’ private data safe without damaging useful medical information. However, k-anonymity cannot prevent sensitive attribute disclosure. An alternative, l-diversity, has been proposed as a solution to this problem and is defined as: each Q-block (ie, each set of rows corresponding to the same value for identifiers) contains at least l well-represented values for each sensitive attribute. While l-diversity protects against sensitive attribute disclosure, it is limited in that it focuses only on diversifying sensitive attributes.
The aim of the study is to develop a k-anonymity method that not only minimizes information loss but also achieves diversity of the sensitive attribute.
This paper proposes a new privacy protection method that uses conditional entropy and mutual information. This method considers both information loss as well as diversity of sensitive attributes. Conditional entropy can measure the information loss by generalization, and mutual information is used to achieve the diversity of sensitive attributes. This method can offer appropriate Q-blocks for generalization.
We used the adult database from the UCI Machine Learning Repository and found that the proposed method can greatly reduce information loss compared with a recent l-diversity study. It can also achieve the diversity of sensitive attributes by counting the number of Q-blocks that have leaks of diversity.
This study provides a privacy protection method that can improve data utility and protect against sensitive attribute disclosure. The method is viable and should be of interest for further privacy protection in EHR applications.},
keywords = {k-anonymity, l-diversity, Medical informatics},
pubstate = {published},
tppubtype = {article}
}
Electronic Health Records (EHRs) enable the sharing of patients’ medical data. Since EHRs include patients’ private data, access by researchers is restricted. Therefore k-anonymity is necessary to keep patients’ private data safe without damaging useful medical information. However, k-anonymity cannot prevent sensitive attribute disclosure. An alternative, l-diversity, has been proposed as a solution to this problem and is defined as: each Q-block (ie, each set of rows corresponding to the same value for identifiers) contains at least l well-represented values for each sensitive attribute. While l-diversity protects against sensitive attribute disclosure, it is limited in that it focuses only on diversifying sensitive attributes.
The aim of the study is to develop a k-anonymity method that not only minimizes information loss but also achieves diversity of the sensitive attribute.
This paper proposes a new privacy protection method that uses conditional entropy and mutual information. This method considers both information loss as well as diversity of sensitive attributes. Conditional entropy can measure the information loss by generalization, and mutual information is used to achieve the diversity of sensitive attributes. This method can offer appropriate Q-blocks for generalization.
We used the adult database from the UCI Machine Learning Repository and found that the proposed method can greatly reduce information loss compared with a recent l-diversity study. It can also achieve the diversity of sensitive attributes by counting the number of Q-blocks that have leaks of diversity.
This study provides a privacy protection method that can improve data utility and protect against sensitive attribute disclosure. The method is viable and should be of interest for further privacy protection in EHR applications.
The aim of the study is to develop a k-anonymity method that not only minimizes information loss but also achieves diversity of the sensitive attribute.
This paper proposes a new privacy protection method that uses conditional entropy and mutual information. This method considers both information loss as well as diversity of sensitive attributes. Conditional entropy can measure the information loss by generalization, and mutual information is used to achieve the diversity of sensitive attributes. This method can offer appropriate Q-blocks for generalization.
We used the adult database from the UCI Machine Learning Repository and found that the proposed method can greatly reduce information loss compared with a recent l-diversity study. It can also achieve the diversity of sensitive attributes by counting the number of Q-blocks that have leaks of diversity.
This study provides a privacy protection method that can improve data utility and protect against sensitive attribute disclosure. The method is viable and should be of interest for further privacy protection in EHR applications.