Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand

被引:0
|
作者
Nhung Nghiem [1 ]
Nick Wilson [2 ]
Jeremy Krebs [1 ]
Truyen Tran [3 ]
机构
[1] University of Otago Wellington,Department of Public Health
[2] Australian National University,John Curtin School of Medical Research
[3] University of Otago Wellington,Department of Medicine
[4] Deakin University,Applied Artificial Intelligence Institute (A2I2)
关键词
Machine learning; Diabetes complications; Cardiovascular disease; Risk prediction; Health and social administrative data;
D O I
10.1186/s12911-024-02678-x
中图分类号
学科分类号
摘要
引用
下载
收藏
相关论文
共 50 条
  • [41] A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data
    Parikh, Ravi B.
    Linn, Kristin A.
    Yan, Jiali
    Maciejewski, Matthew L.
    Rosland, Ann-Marie
    Volpp, Kevin G.
    Groeneveld, Peter W.
    Navathe, Amol S.
    PLOS ONE, 2021, 16 (02):
  • [42] Machine learning risk estimation and prediction of death in continuing care facilities using administrative data
    Shahidi, Faezehsadat
    Rennert-May, Elissa
    D'Souza, Adam G.
    Crocker, Alysha
    Faris, Peter
    Leal, Jenine
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [43] Machine learning risk estimation and prediction of death in continuing care facilities using administrative data
    Faezehsadat Shahidi
    Elissa Rennert-May
    Adam G. D’Souza
    Alysha Crocker
    Peter Faris
    Jenine Leal
    Scientific Reports, 13
  • [44] Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets
    Nhung Nghiem
    June Atkinson
    Binh P. Nguyen
    An Tran-Duy
    Nick Wilson
    Health Economics Review, 13
  • [45] Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets
    Nghiem, Nhung
    Atkinson, June
    Nguyen, Binh P.
    Tran-Duy, An
    Wilson, Nick
    HEALTH ECONOMICS REVIEW, 2023, 13 (01)
  • [46] Social impacts and costs of schizophrenia: a national cohort study using New Zealand linked administrative data
    Gibb, Sheree
    Brewer, Naomi
    Bowden, Nicholas
    NEW ZEALAND MEDICAL JOURNAL, 2021, 134 (1537) : 66 - 83
  • [47] Using machine learning to explore the efficacy of administrative variables in prediction of subjective-wellbeing outcomes in New Zealand
    Anantha Narayanan
    Tom Stewart
    Scott Duncan
    Gail Pacheco
    Scientific Reports, 15 (1)
  • [48] Predicting Health Outcomes Using Machine Learning in Pediatric Heart Transplantation Using UNOS Data
    Killian, M. O.
    Tian, S.
    Xing, A.
    Gupta, D.
    He, Z.
    JOURNAL OF HEART AND LUNG TRANSPLANTATION, 2023, 42 (04): : S22 - S22
  • [49] Predicting Asthma Exacerbation Risk in the Adult South Korean Population Using Integrated Health Data and Machine Learning Models
    Choi, Joon Young
    Rhee, Chin Kook
    JOURNAL OF ASTHMA AND ALLERGY, 2024, 17 : 783 - 789
  • [50] A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study
    Chen, Min
    Tan, Xuan
    Padman, Rema
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2023, 25