Predicting Patient Mortality: Using Machine Learning to Identify At-Risk Patients and Improve Outcomes

被引：0
|
作者：
Barton, C.
Mohamadlou, H.
Lynn-Palevsky, A.
Fletcher, G.
Shieh, L.
Stark, P.
Chettipally, U.
Shimabukuro, D. W.
Feldman, M.
Das, R.
机构：
来源：
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE | 2018年 / 197卷
基金：
美国国家卫生研究院;
关键词：
D O I：
暂无
中图分类号：
R4 [临床医学];
学科分类号：
1002 ; 100602 ;
摘要：
A4299
引用
收藏
页数：2
相关论文
共 50 条
  • [1] Using machine learning to identify the most at-risk students in physics classes
    Yang, Jie
    DeVore, Seth
    Hewagallage, Dona
    Miller, Paul
    Ryan, Qing X.
    Stewart, John
    PHYSICAL REVIEW PHYSICS EDUCATION RESEARCH, 2020, 16 (02):
  • [2] Predicting mortality in hemodialysis patients using machine learning analysis
    Garcia-Montemayor, Victoria
    Martin-Malo, Alejandro
    Barbieri, Carlo
    Bellocchio, Francesco
    Soriano, Sagrario
    Pendon-Ruiz de Mier, Victoria
    Molina, Ignacio R.
    Aljama, Pedro
    Rodriguez, Mariano
    CLINICAL KIDNEY JOURNAL, 2021, 14 (05) : 1388 - 1395
  • [3] Machine Learning Models Predicting Cardiovascular and Renal Outcomes and Mortality in Patients with Hyperkalemia
    Kanda, Eiichiro
    Okami, Suguru
    Kohsaka, Shun
    Okada, Masafumi
    Ma, Xiaojun
    Kimura, Takeshi
    Shirakawa, Koichi
    Yajima, Toshitaka
    NUTRIENTS, 2022, 14 (21)
  • [4] Predicting mortality in systemic sclerosis patients using machine learning approaches
    Jang, A.
    Patel, S.
    Patel, S.
    Shah, S.
    Lio, P.
    JOURNAL OF INVESTIGATIVE DERMATOLOGY, 2023, 143 (05) : S37 - S37
  • [5] Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms
    Afrash M.R.
    Shanbehzadeh M.
    Kazemi-Arpanahi H.
    Journal of Biomedical Physics and Engineering, 2022, 12 (06): : 611 - 626
  • [6] An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning
    Chowdhury, Muhammad E. H.
    Rahman, Tawsifur
    Khandakar, Amith
    Al-Madeed, Somaya
    Zughaier, Susu M.
    Doi, Suhail A. R.
    Hassen, Hanadi
    Islam, Mohammad T.
    COGNITIVE COMPUTATION, 2024, 16 (04) : 1778 - 1793
  • [7] Using PROs and machine learning to identify "at risk" patients for musculoskeletal injury
    Baumhauer, Judith
    Mitten, David
    Vasalos, Kostantinos
    QUALITY OF LIFE RESEARCH, 2018, 27 : S9 - S9
  • [8] Prediction of maternal hemorrhage: using machine learning to identify patients at risk
    Westcott, Jill M.
    Hughes, Francine
    Liu, Wenke
    Grivainis, Mark
    Keefe, David L.
    Hoskins, Iffath A.
    Fenyo, David
    AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY, 2020, 222 (01) : S407 - S407
  • [9] Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning
    Hassan, Abbas M.
    Biaggi-Ondina, Andrea
    Rajesh, Aashish
    Asaad, Malke
    Nelson, Jonas A.
    Coert, J. Henk
    Mehrara, Babak J.
    Butler, Charles E.
    AMERICAN SURGEON, 2023, 89 (01) : 31 - 35
  • [10] Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission
    Geneves, Pierre
    Calmant, Thomas
    Layaida, Nabil
    Lepelley, Marion
    Artemova, Svetlana
    Bosson, Jean-Luc
    BIG DATA RESEARCH, 2018, 12 : 23 - 34
12345