Application of machine learning techniques to understand ethnic differences and risk factors for incident chronic kidney disease in Asians

10.1136/bmjdrc-2021-002364

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Main Authors: Lim, Cynthia Ciwei, He, Feng, Li, Jialiang, Tham, Yih Chung, Tan, Chieh Suai, Cheng, Ching-Yu, Wong, Tien-Yin, Sabanayagam, Charumathi
Other Authors: STATISTICS AND DATA SCIENCE
Format: Article
Language:English
Published: BMJ PUBLISHING GROUP 2024
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Online Access:https://scholarbank.nus.edu.sg/handle/10635/248895
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Institution: National University of Singapore
Language: English
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spelling sg-nus-scholar.10635-2488952024-11-15T00:32:33Z Application of machine learning techniques to understand ethnic differences and risk factors for incident chronic kidney disease in Asians Lim, Cynthia Ciwei He, Feng Li, Jialiang Tham, Yih Chung Tan, Chieh Suai Cheng, Ching-Yu Wong, Tien-Yin Sabanayagam, Charumathi STATISTICS AND DATA SCIENCE DUKE-NUS MEDICAL SCHOOL OPHTHALMOLOGY DEAN'S OFFICE (DUKE-NUS MEDICAL SCHOOL) Assoc Prof Charumathi Sabanayagam Science & Technology Life Sciences & Biomedicine Endocrinology & Metabolism DIABETES-MELLITUS HEALTH LITERACY BLOOD-PRESSURE EYE EPIDEMIOLOGY METHODOLOGY IMPACT COHORT MODEL ONSET 10.1136/bmjdrc-2021-002364 BMJ OPEN DIABETES RESEARCH & CARE 9 2 2024-06-14T01:11:31Z 2024-06-14T01:11:31Z 2021-12 2024-06-11T03:37:52Z Article Lim, Cynthia Ciwei, He, Feng, Li, Jialiang, Tham, Yih Chung, Tan, Chieh Suai, Cheng, Ching-Yu, Wong, Tien-Yin, Sabanayagam, Charumathi (2021-12). Application of machine learning techniques to understand ethnic differences and risk factors for incident chronic kidney disease in Asians. BMJ OPEN DIABETES RESEARCH & CARE 9 (2). ScholarBank@NUS Repository. https://doi.org/10.1136/bmjdrc-2021-002364 2052-4897 https://scholarbank.nus.edu.sg/handle/10635/248895 en BMJ PUBLISHING GROUP Elements
institution National University of Singapore
building NUS Library
continent Asia
country Singapore
Singapore
content_provider NUS Library
collection ScholarBank@NUS
language English
topic Science & Technology
Life Sciences & Biomedicine
Endocrinology & Metabolism
DIABETES-MELLITUS
HEALTH LITERACY
BLOOD-PRESSURE
EYE
EPIDEMIOLOGY
METHODOLOGY
IMPACT
COHORT
MODEL
ONSET
spellingShingle Science & Technology
Life Sciences & Biomedicine
Endocrinology & Metabolism
DIABETES-MELLITUS
HEALTH LITERACY
BLOOD-PRESSURE
EYE
EPIDEMIOLOGY
METHODOLOGY
IMPACT
COHORT
MODEL
ONSET
Lim, Cynthia Ciwei
He, Feng
Li, Jialiang
Tham, Yih Chung
Tan, Chieh Suai
Cheng, Ching-Yu
Wong, Tien-Yin
Sabanayagam, Charumathi
Application of machine learning techniques to understand ethnic differences and risk factors for incident chronic kidney disease in Asians
description 10.1136/bmjdrc-2021-002364
author2 STATISTICS AND DATA SCIENCE
author_facet STATISTICS AND DATA SCIENCE
Lim, Cynthia Ciwei
He, Feng
Li, Jialiang
Tham, Yih Chung
Tan, Chieh Suai
Cheng, Ching-Yu
Wong, Tien-Yin
Sabanayagam, Charumathi
format Article
author Lim, Cynthia Ciwei
He, Feng
Li, Jialiang
Tham, Yih Chung
Tan, Chieh Suai
Cheng, Ching-Yu
Wong, Tien-Yin
Sabanayagam, Charumathi
author_sort Lim, Cynthia Ciwei
title Application of machine learning techniques to understand ethnic differences and risk factors for incident chronic kidney disease in Asians
title_short Application of machine learning techniques to understand ethnic differences and risk factors for incident chronic kidney disease in Asians
title_full Application of machine learning techniques to understand ethnic differences and risk factors for incident chronic kidney disease in Asians
title_fullStr Application of machine learning techniques to understand ethnic differences and risk factors for incident chronic kidney disease in Asians
title_full_unstemmed Application of machine learning techniques to understand ethnic differences and risk factors for incident chronic kidney disease in Asians
title_sort application of machine learning techniques to understand ethnic differences and risk factors for incident chronic kidney disease in asians
publisher BMJ PUBLISHING GROUP
publishDate 2024
url https://scholarbank.nus.edu.sg/handle/10635/248895
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