Deploying machine learning algorithms to stratify patients with inflammatory bowel disease using routinely collected electronic health records

被引:0
|
作者
Mian, H. A. [1 ]
Radia, C. [2 ]
Sarras, K. [3 ]
Hayee, B. [4 ]
Al-Agil, M. [5 ]
Patel, M. [3 ]
Kent, A. [4 ]
Dubois, P. [3 ]
Pavlidis, P. [6 ]
Iniesta, R. [1 ]
机构
[1] Kings Coll London, Dept Biostat & Hlth Informat, London, England
[2] Kings Coll London, Dept Gastroenterol, IBD Serv, London, England
[3] Kings Coll Hosp London, Dept Gastroenterol, London, England
[4] Kings Coll Hosp London, Gatroenterol, London, England
[5] Kings Coll Hosp London, Cogstalk, London, England
[6] Kings Coll HospitalNHS Fdn Trust, Dept Gastroenterol, IBD Serv, London, England
来源
关键词
D O I
暂无
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
P455
引用
收藏
页码:I913 / I913
页数:1
相关论文
共 50 条
  • [31] Using Machine Learning and Electronic Health Records to Predict Postpartum Depression
    Zhang, Yiye
    Joly, Rochelle
    Hermann, Alison
    Pathak, Jyotishman
    OBSTETRICS AND GYNECOLOGY, 2020, 135 : 59S - 60S
  • [32] Using machine learning to detect sarcopenia from electronic health records
    Luo, Xiao
    Ding, Haoran
    Broyles, Andrea
    Warden, Stuart J.
    Moorthi, Ranjani N.
    Imel, Erik A.
    DIGITAL HEALTH, 2023, 9
  • [33] Descriptive and Predictive Analytics on Electronic Health Records using Machine Learning
    Anandi, V
    Ramesh, M.
    2022 SECOND INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRICAL, COMPUTING, COMMUNICATION AND SUSTAINABLE TECHNOLOGIES (ICAECT), 2022,
  • [34] Evaluation of algorithms to identify patients with Fabry disease using routinely collected hospital activity data
    Evison, Felicity
    Franks, Daniel
    Gallier, Suzy
    Geberhiwot, Tarekegn
    MOLECULAR GENETICS AND METABOLISM, 2021, 132 (02) : S38 - S38
  • [35] Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records
    Manzini, Enrico
    Vlacho, Bogdan
    Franch-Nadal, Josep
    Escudero, Joan
    Genova, Ana
    Reixach, Elisenda
    Andres, Erik
    Pizarro, Israel
    Portero, Jose-Luis
    Mauricio, Didac
    Perera-Lluna, Alexandre
    JOURNAL OF BIOMEDICAL INFORMATICS, 2022, 135
  • [36] THE USE OF MACHINE LEARNING IN ELECTRONIC HEALTH RECORDS DISEASE ANALYSIS: AN UPDATED PERSPECTIVE
    Cossio, C. M.
    Gilardino, R.
    VALUE IN HEALTH, 2022, 25 (07) : S576 - S577
  • [37] Diabetes and the direct secondary use of electronic health records: Using routinely collected and stored data to drive research and understanding
    Robbins, Tim
    Keung, Sarah N. Lim Choi
    Sankar, Sailesh
    Randeva, Harpal
    Arvanitis, Theodoros N.
    DIGITAL HEALTH, 2018, 4
  • [38] Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records
    Wang, Yanshan
    Zhao, Yiqing
    Therneau, Terry M.
    Atkinson, Elizabeth J.
    Tafti, Ahmad P.
    Zhang, Nan
    Amin, Shreyasee
    Limper, Andrew H.
    Khosla, Sundeep
    Liu, Hongfang
    JOURNAL OF BIOMEDICAL INFORMATICS, 2020, 102
  • [39] Predicting Wilson disease progression using machine learning with real-world electronic health records
    Liang, Caihua
    Kelly, Scott
    Shen, Rongjun
    Li, Ling
    Lobello, Kasia
    Arkin, Steven
    Huang, Kui
    Zhou, Xiaofeng
    PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2022, 31 : 63 - 64
  • [40] PREDICTORS OF DISEASE MODIFYING THERAPY INITIATION IN PATIENTS WITH MULTIPLE SCLEROSIS USING ELECTRONIC HEALTH RECORDS DATA - A MACHINE LEARNING PERSPECTIVE
    Icten, Z.
    Hitchcock, C.
    Davis, S.
    Ciofani, D.
    Sanky, M.
    Hadzi, T.
    Khalil, I
    Alas, V
    VALUE IN HEALTH, 2017, 20 (05) : A1 - A2