Using Breathing Features and 3 Vital Signs in Machine Learning to Predict Acute Respiratory Deterioration

被引：0
|
作者：
Singh, G. [1 ]
Liang, L. Gen [2 ]
Pyng, L. [3 ]
机构：
[1] Respiree Pte Ltd, Singapore, Singapore
[2] Inst Infocomm Res, Singapore, Singapore
[3] Natl Univ Singapore Hosp, Resp & Crit Care Med, Singapore, Singapore
来源：
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE | 2022年 / 205卷
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：
A1491
引用
收藏
页数：2
相关论文
共 50 条
  • [21] Using machine learning to predict deterioration of symptoms in COPD patients within a telemonitoring program
    Moraza, Javier
    Esteban-Aizpiri, Cristobal
    Aramburu, Amaia
    Garcia, Pedro
    Sancho, Fernando
    Resino, Sergio
    Chasco, Leyre
    Conde, Francisco Jose
    Gutierrez, Jose Antonio
    Santano, Dabi
    Esteban, Cristobal
    SCIENTIFIC REPORTS, 2025, 15 (01):
  • [22] An Ensemble of Machine Learning and Clinician Set Thresholds for Vital Signs Alarms
    Mai, Shenhan
    Venki, Balasubramanian
    Teena, Arora
    2022 AUSTRALIAN COMPUTER SCIENCE WEEK (ACSW 2022), 2022, : 232 - 234
  • [23] Effective Features to Predict Residential Energy Consumption Using Machine Learning
    Mo, Yunjeong
    Zhao, Dong
    Syal, Matt
    COMPUTING IN CIVIL ENGINEERING 2019: SMART CITIES, SUSTAINABILITY, AND RESILIENCE, 2019, : 284 - 291
  • [24] Machine Learning to predict cetacean behaviour using social and environmental features
    Cherubini, Carla
    Saccotelli, Leonardo
    Caccioppoli, Rocco
    Fanizza, Carmelo
    Santacesaria, Francesca Cornelia
    Lecci, Rita
    Causio, Salvatore
    Federico, Ivan
    Cipriano, Giulia
    Dimauro, Giovanni
    Coppini, Giovanni
    Bellomo, Stefano
    Carlucci, Roberto
    Maglietta, Rosalia
    2023 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR THE SEA; LEARNING TO MEASURE SEA HEALTH PARAMETERS, METROSEA, 2023, : 289 - 293
  • [25] Using weak signals to predict spontaneous breathing trial success: a machine learning approach
    Lombardi, Romain
    Jozwiak, Mathieu
    Dellamonica, Jean
    Pasquier, Claude
    INTENSIVE CARE MEDICINE EXPERIMENTAL, 2025, 13 (01):
  • [26] Machine learning model of facial expression outperforms models using analgesia nociception index and vital signs to predict postoperative pain intensity: a pilot study
    Park, Insun
    Park, Jae Hyon
    Yoon, Jongjin
    Na, Hyo-Seok
    Oh, Ah-Young
    Ryu, Junghee
    Koo, Bon-Wook
    KOREAN JOURNAL OF ANESTHESIOLOGY, 2024, 77 (02) : 195 - 204
  • [27] Importance of general adiposity, visceral adiposity and vital signs in predicting blood biomarkers using machine learning
    Zhou, Weihong
    Wang, Yingjie
    Gu, Xiaoping
    Feng, Zhong-Ping
    Lee, Kang
    Peng, Yuzhu
    Barszczyk, Andrew
    INTERNATIONAL JOURNAL OF CLINICAL PRACTICE, 2021, 75 (01)
  • [28] Tracking vital signs of a patient using channel state information and machine learning for a smart healthcare system
    Khan, Muhammad Imran
    Jan, Mian Ahmad
    Muhammad, Yar
    Do, Dinh-Thuan
    Rehman, Ateeq Ur
    Mavromoustakis, Constandinos X.
    Pallis, Evangelos
    NEURAL COMPUTING & APPLICATIONS, 2021,
  • [29] Detecting acute respiratory diseases in the pediatric population using cough sound features and machine learning: A systematic review
    Sharan, Roneel V.
    Rahimi-Ardabili, Hania
    INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2023, 176
  • [30] Development and Validation of a Machine Learning Algorithm Using Clinical Pages to Predict Imminent Clinical Deterioration
    Bryan D. Steitz
    Allison B. McCoy
    Thomas J. Reese
    Siru Liu
    Liza Weavind
    Kipp Shipley
    Elise Russo
    Adam Wright
    Journal of General Internal Medicine, 2024, 39 : 27 - 35
12345