Machine Learning-Based Models for Prediction of Critical Illness at Community, Paramedic, and Hospital Stages
Overcrowding of emergency department (ED) has put a strain on national healthcare systems and adversely affected the clinical outcomes of critically ill patients. Early identification of critically ill patients prior to ED visits can help induce optimal patient flow and allocate medical resources ef...
Main Authors: | Sijin Lee, Hyun Ji Park, Jumi Hwang, Sung Woo Lee, Kap Su Han, Won Young Kim, Jinwoo Jeong, Hyunggoo Kang, Armi Kim, Chulung Lee, Su Jin Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2023-01-01
|
Series: | Emergency Medicine International |
Online Access: | http://dx.doi.org/10.1155/2023/1221704 |
Similar Items
-
Emergency department utilization and risk factors for mortality in older patients: an analysis of Korean National Emergency Department Information System data
by: Soyoon Kim, et al.
Published: (2021-06-01) -
A Propensity Score-Matched Comparison of In-Hospital Mortality between Dedicated Regional Trauma Centers and Emergency Medical Centers in the Republic of Korea
by: Yuri Choi, et al.
Published: (2022-01-01) -
Prolonged Length of Stay in the Emergency Department and Increased Risk of In-Hospital Cardiac Arrest: A nationwide Population-Based Study in South Korea, 2016–2017
by: June-sung Kim, et al.
Published: (2020-07-01) -
Development and validation of a scoring system for mortality prediction and application of standardized W statistics to assess the performance of emergency departments
by: Jinwoo Jeong, et al.
Published: (2021-06-01) -
Identification of Promising Vacant Technologies for the Development of Truck on Freight Train Transportation Systems
by: Sungchan Jun, et al.
Published: (2021-01-01)