The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
정밀의학의 비전을 실현하기 위해서는 근거기반의학이 핵심 요소입니다. 뇌동맥류와 같은 복잡한 질병의 자연사를 이해하고 특히 파열 위험 요인에 대한 증거를 조사하는 것은 임상 시험, 생존 분석 및 결과 예측을 수행하기 위한 의미론적 데이터 준비 기술의 존재에 달려 있습니다. 신경질환 분야의 맞춤형 의료를 위해서는 여러 보건 기관이 협력하고 증거 기반 관찰 연구를 수행하는 것이 매우 중요합니다. 조직 내 수준에서 관찰 연구를 수행하기 위해 개인 정보 보호 및 의미 기반 데이터 준비 프로세스를 자동화하는 수단이 없으면 데이터를 수동으로 준비하는 데 몇 달이 걸릴 것입니다. 따라서 본 논문에서는 의미론적 및 개인 정보 보호가 가능한 다자간 데이터 준비 아키텍처와 79계층 의미론적 유사성 알고리즘을 제안합니다. 평가 결과 제안된 알고리즘은 83%의 정밀도, 81%의 높은 재현율, XNUMX%의 F-measure를 달성한 것으로 나타났습니다.
Khalid Mahmood MALIK
Oakland University
Hisham KANAAN
Oakland University
Vian SABEEH
Oakland University
Ghaus MALIK
Henry Ford Hospital
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부
Khalid Mahmood MALIK, Hisham KANAAN, Vian SABEEH, Ghaus MALIK, "Autonomous, Decentralized and Privacy-Enabled Data Preparation for Evidence-Based Medicine with Brain Aneurysm as a Phenotype" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 8, pp. 1787-1797, August 2018, doi: 10.1587/transcom.2017ADP0007.
Abstract: To enable the vision of precision medicine, evidence-based medicine is the key element. Understanding the natural history of complex diseases like brain aneurysm and particularly investigating the evidences of its rupture risk factors relies on the existence of semantic-enabled data preparation technology to conduct clinical trials, survival analysis and outcome prediction. For personalized medicine in the field of neurological diseases, it is very important that multiple health organizations coordinate and cooperate to conduct evidence based observational studies. Without the means of automating the process of privacy and semantic-enabled data preparation to conduct observational studies at intra-organizational level would require months to manually prepare the data. Therefore, this paper proposes a semantic and privacy enabled, multi-party data preparation architecture and a four-tiered semantic similarity algorithm. Evaluation shows that proposed algorithm achieves a precision of 79%, high recall at 83% and F-measure of 81%.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017ADP0007/_p
부
@ARTICLE{e101-b_8_1787,
author={Khalid Mahmood MALIK, Hisham KANAAN, Vian SABEEH, Ghaus MALIK, },
journal={IEICE TRANSACTIONS on Communications},
title={Autonomous, Decentralized and Privacy-Enabled Data Preparation for Evidence-Based Medicine with Brain Aneurysm as a Phenotype},
year={2018},
volume={E101-B},
number={8},
pages={1787-1797},
abstract={To enable the vision of precision medicine, evidence-based medicine is the key element. Understanding the natural history of complex diseases like brain aneurysm and particularly investigating the evidences of its rupture risk factors relies on the existence of semantic-enabled data preparation technology to conduct clinical trials, survival analysis and outcome prediction. For personalized medicine in the field of neurological diseases, it is very important that multiple health organizations coordinate and cooperate to conduct evidence based observational studies. Without the means of automating the process of privacy and semantic-enabled data preparation to conduct observational studies at intra-organizational level would require months to manually prepare the data. Therefore, this paper proposes a semantic and privacy enabled, multi-party data preparation architecture and a four-tiered semantic similarity algorithm. Evaluation shows that proposed algorithm achieves a precision of 79%, high recall at 83% and F-measure of 81%.},
keywords={},
doi={10.1587/transcom.2017ADP0007},
ISSN={1745-1345},
month={August},}
부
TY - JOUR
TI - Autonomous, Decentralized and Privacy-Enabled Data Preparation for Evidence-Based Medicine with Brain Aneurysm as a Phenotype
T2 - IEICE TRANSACTIONS on Communications
SP - 1787
EP - 1797
AU - Khalid Mahmood MALIK
AU - Hisham KANAAN
AU - Vian SABEEH
AU - Ghaus MALIK
PY - 2018
DO - 10.1587/transcom.2017ADP0007
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2018
AB - To enable the vision of precision medicine, evidence-based medicine is the key element. Understanding the natural history of complex diseases like brain aneurysm and particularly investigating the evidences of its rupture risk factors relies on the existence of semantic-enabled data preparation technology to conduct clinical trials, survival analysis and outcome prediction. For personalized medicine in the field of neurological diseases, it is very important that multiple health organizations coordinate and cooperate to conduct evidence based observational studies. Without the means of automating the process of privacy and semantic-enabled data preparation to conduct observational studies at intra-organizational level would require months to manually prepare the data. Therefore, this paper proposes a semantic and privacy enabled, multi-party data preparation architecture and a four-tiered semantic similarity algorithm. Evaluation shows that proposed algorithm achieves a precision of 79%, high recall at 83% and F-measure of 81%.
ER -