Results of 20 Machine-Learning Techniques to Identify Sepsis Patients in the Emergency Department

被引：1
|
作者：
Sherwin, R. [1 ]
Ying, H. [1 ]
机构：
[1] Wayne State Univ, Detroit, MI USA
来源：
ANNALS OF EMERGENCY MEDICINE | 2018年 / 72卷 / 04期
关键词：
D O I：
10.1016/j.annemergmed.2018.08.019
中图分类号：
R4 [临床医学];
学科分类号：
1002 ; 100602 ;
摘要：
14
引用
收藏
页码：S6 / S7
页数：2
相关论文
共 50 条
  • [21] Machine learning to identify attributes that predict patients who leave without being seen in a pediatric emergency department
    Sarty, Julia
    Fitzpatrick, Eleanor A.
    Taghavi, Majid
    T. VanBerkel, Peter
    Hurley, Katrina F.
    CANADIAN JOURNAL OF EMERGENCY MEDICINE, 2023, 25 (08) : 689 - 694
  • [22] Machine learning to identify attributes that predict patients who leave without being seen in a pediatric emergency department
    Julia Sarty
    Eleanor A. Fitzpatrick
    Majid Taghavi
    Peter T. VanBerkel
    Katrina F. Hurley
    Canadian Journal of Emergency Medicine, 2023, 25 : 689 - 694
  • [23] Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database
    Ji Hoon Kim
    Arom Choi
    Min Joung Kim
    Heejung Hyun
    Sunhee Kim
    Hyuk-Jae Chang
    Scientific Reports, 12 (1)
  • [24] Predicting 90-day return to the emergency department in orthopaedic trauma patients in the Southeastern USA: a machine-learning approach
    Valan, Bruno
    Therien, Aaron
    Peairs, Emily
    Ayehu, Solomon
    Taylor, Joshua
    Zeng, Daniel
    Olson, Steven
    Reilly, Rachel
    Pean, Christian
    Debaun, Malcolm
    INJURY PREVENTION, 2025,
  • [25] Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database
    Kim, Ji Hoon
    Choi, Arom
    Kim, Min Joung
    Hyun, Heejung
    Kim, Sunhee
    Chang, Hyuk-Jae
    SCIENTIFIC REPORTS, 2022, 12 (01):
  • [26] Sepsis prediction using machine-learning methods: prolonged disorders of consciousness patients
    Metsker, O.
    Aybazova, M.
    Kondratyeva, E.
    Dryagina, N.
    Kondratev, A.
    Efimov, E.
    JOURNAL OF THE NEUROLOGICAL SCIENCES, 2019, 405
  • [27] Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression
    Hasegawa, Daisuke
    Yamakawa, Kazuma
    Nishida, Kazuki
    Okada, Naoki
    Murao, Shuhei
    Nishida, Osamu
    JOURNAL OF CLINICAL MEDICINE, 2020, 9 (07) : 1 - 10
  • [28] A Machine-Learning Sepsis Prediction Model for Patients Undergoing Hematopoietic Cell Transplantation
    Dadwal, Sanjeet
    Eftekhari, Zahra
    Thomas, Tushondra
    Johnson, Deron
    Yang, Dongyun
    Mokhtari, Sally
    Nikolaenko, Liana
    Munu, Janet
    Nakamura, Ryotaro
    BLOOD, 2018, 132
  • [29] Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study
    Brossard, Cyrielle
    Goetz, Christophe
    Catoire, Pierre
    Cipolat, Lauriane
    Guyeux, Christophe
    Jardine, Cedric Gil
    Akplogan, Mahuna
    Vuillaume, Laure Abensur
    BMC EMERGENCY MEDICINE, 2025, 25 (01):
  • [30] Predicting progression to septic shock in the emergency department using an externally generalizable machine-learning algorithm
    Gil-Jardine, C.
    ANNALES FRANCAISES DE MEDECINE D URGENCE, 2021, 11 (04): : 271 - 272
12345