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
Untuk membolehkan visi perubatan ketepatan, perubatan berasaskan bukti adalah elemen utama. Memahami sejarah semula jadi penyakit kompleks seperti aneurisma otak dan khususnya menyiasat bukti faktor risiko pecahnya bergantung pada kewujudan teknologi penyediaan data yang didayakan semantik untuk menjalankan ujian klinikal, analisis kelangsungan hidup dan ramalan hasil. Untuk perubatan yang diperibadikan dalam bidang penyakit saraf, adalah sangat penting bahawa pelbagai organisasi kesihatan menyelaras dan bekerjasama untuk menjalankan kajian pemerhatian berasaskan bukti. Tanpa cara mengautomasikan proses privasi dan penyediaan data yang didayakan semantik untuk menjalankan kajian pemerhatian di peringkat intra-organisasi akan memerlukan beberapa bulan untuk menyediakan data secara manual. Oleh itu, kertas kerja ini mencadangkan seni bina penyediaan data berbilang pihak yang didayakan semantik dan privasi dan algoritma persamaan semantik empat peringkat. Penilaian menunjukkan bahawa algoritma yang dicadangkan mencapai ketepatan 79%, ingatan semula tinggi pada 83% dan ukuran F sebanyak 81%.
Khalid Mahmood MALIK
Oakland University
Hisham KANAAN
Oakland University
Vian SABEEH
Oakland University
Ghaus MALIK
Henry Ford Hospital
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Salinan
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
Salinan
@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},}
Salinan
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 -