Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach

被引：0
|
作者：
Saeed Kayhanian
Adam M. H. Young
Chaitanya Mangla
Ibrahim Jalloh
Helen M. Fernandes
Matthew R. Garnett
Peter J. Hutchinson
Shruti Agrawal
机构：
[1] University of Cambridge,Department of Clinical Neurosciences, Division of Neurosurgery, Addenbrooke’s Hospital
[2] University of Cambridge,Fitzwilliam College
[3] University of Cambridge,Department of Computer Science and Technology
[4] University of Cambridge,Department of Paediatric Intensive Care, Addenbrooke’s Hospital
来源：
Pediatric Research | 2019年 / 86卷
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：641 / 645
页数：4
相关论文
共 50 条
  • [41] Statistical models for prediction of outcomes after traumatic brain injury based on patients admission characteristics
    Kamal, Vineet Kumar
    Agrawal, Deepak
    Pandey, R. M.
    BRAIN INJURY, 2014, 28 (5-6) : 546 - 547
  • [42] The quest for a biological phenotype of adolescent non-suicidal self-injury: a machine-learning approach
    Ines Mürner-Lavanchy
    Julian Koenig
    Corinna Reichl
    Johannes Josi
    Marialuisa Cavelti
    Michael Kaess
    Translational Psychiatry, 14
  • [43] The quest for a biological phenotype of adolescent non-suicidal self-injury: a machine-learning approach
    Murner-Lavanchy, Ines
    Koenig, Julian
    Reichl, Corinna
    Josi, Johannes
    Cavelti, Marialuisa
    Kaess, Michael
    TRANSLATIONAL PSYCHIATRY, 2024, 14 (01)
  • [44] A Machine Learning Approach for the Prediction of Severe Acute Kidney Injury Following Traumatic Brain Injury
    Peng, Chi
    Yang, Fan
    Li, Lulu
    Peng, Liwei
    Yu, Jian
    Wang, Peng
    Jin, Zhichao
    NEUROCRITICAL CARE, 2023, 38 (02) : 335 - 344
  • [45] A Machine Learning Approach for the Prediction of Severe Acute Kidney Injury Following Traumatic Brain Injury
    Chi Peng
    Fan Yang
    Lulu Li
    Liwei Peng
    Jian Yu
    Peng Wang
    Zhichao Jin
    Neurocritical Care, 2023, 38 : 335 - 344
  • [46] Learning from the past: a machine-learning approach for predicting the resilience of locked-in regions after a natural shock
    Fantechi, Federico
    Modica, Marco
    REGIONAL STUDIES, 2023, 57 (12) : 2537 - 2550
  • [47] Robust Machine-Learning Technique for Prediction of Late Mortality After Myocardial Injury in Noncardiac Surgery Patients
    Johnson, Bijoy
    Francis, Johnson
    AMERICAN JOURNAL OF CARDIOLOGY, 2023, 204 : 428 - 429
  • [48] Predicting Postoperative Mortality After Metastatic Intraspinal Neoplasm Excision: Development of a Machine-Learning Approach
    DiSilvestro, Kevin J.
    Veeramani, Ashwin
    McDonald, Christopher L.
    Zhang, Andrew S.
    Kuris, Eren O.
    Durand, Wesley M.
    Cohen, Eric M.
    Daniels, Alan H.
    WORLD NEUROSURGERY, 2021, 146 : E917 - E924
  • [49] Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality
    Cho, Jaehyeong
    Park, Jimyung
    Jeong, Eugene
    Shin, Jihye
    Ahn, Sangjeong
    Park, Min Geun
    Park, Rae Woong
    Park, Yongkeun
    JOURNAL OF PERSONALIZED MEDICINE, 2021, 11 (12):
  • [50] Inflammation gene signature to predict survival outcomes in hepatocellular carcinoma (HCC): A machine-learning approach.
    Al-Bzour, Nour
    Sahin, Ibrahim Halil
    Cavalcante, Ludimila
    Singh, Meghana
    Al-Bzour, Ayah N.
    Saeed, Azhar
    Saeed, Anwaar
    JOURNAL OF CLINICAL ONCOLOGY, 2024, 42 (3_SUPPL) : 551 - 551
12345