Longitudinal profile of routine biomarkers for mortality prediction using unsupervised clustering algorithm in severely burned patients: a retrospective cohort study with prospectively collected data-Reference-Cited by-同舟云学术

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Longitudinal profile of routine biomarkers for mortality prediction using unsupervised clustering algorithm in severely burned patients: a retrospective cohort study with prospectively collected data

Published:2023 Issue:2 Volume:104 Page:126
ISSN:2288-6575
Container-title:Annals of Surgical Treatment and Research
language:en
Short-container-title:Ann Surg Treat Res

Author:

Yoon Jaechul¹^ORCID,Kym Dohern¹²^ORCID,Hur Jun¹^ORCID,Cho Yong-Suk¹^ORCID,Chun Wook¹²^ORCID,Yoon Dogeon²^ORCID

Affiliation:

1. Department of Surgery and Critical Care, Burn Center, Hangang Sacred Heart Hospital, Hallym University Medical Center, Seoul, Korea.

2. Burn Institutes, Hangang Sacred Heart Hospital, Hallym University Medical Center, Seoul, Korea.

Publisher

XMLink

Subject

Surgery

Link

https://astr.or.kr/pdf/10.4174/astr.2023.104.2.126

Reference18 articles.

1. Detection of Infection and Sepsis in Burns

2. Estimation of the distribution of longitudinal biomarker trajectories prior to disease progression

3. CopyMean: A new method to predict monotone missing values in longitudinal studies

4. Development of a risk prediction model (Hangang) and comparison with clinical severity scores in burn patients

5. kmlShape: An Efficient Method to Cluster Longitudinal Data (Time-Series) According to Their Shapes

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