PMID- 35317245
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 2352-9148 (Print)
IS  - 2352-9148 (Electronic)
IS  - 2352-9148 (Linking)
VI  - 30
DP  - 2022
TI  - Using decision tree algorithms for estimating ICU admission of COVID-19 patients.
PG  - 100919
LID - 10.1016/j.imu.2022.100919 [doi]
AB  - INTRODUCTION: Coronavirus disease 2019 (COVID-19) outbreak has overwhelmed many 
      healthcare systems worldwide and put them at the edge of collapsing. As intensive 
      care unit (ICU) capacities are limited, deciding on the proper allocation of 
      required resources is crucial. This study aimed to develop and compare models for 
      early predicting ICU admission in COVID-19 patients at the point of hospital 
      admission. MATERIALS AND METHODS: Using a single-center registry, we studied the 
      records of 512 COVID-19 patients. First, the most important variables were 
      identified using Chi-square test (at p < 0.01) and logistic regression (with odds 
      ratio at P < 0.05). Second, we trained seven decision tree (DT) algorithms 
      (decision stump (DS), Hoeffding tree (HT), LMT, J-48, random forest (RF), random 
      tree (RT) and REP-Tree) using the selected variables. Finally, the models' 
      performance was evaluated. Furthermore, we used an external dataset to validate 
      the prediction models. RESULTS: Using the Chi-square test, 20 important variables 
      were identified. Then, 12 variables were selected for model construction using 
      logistic regression. Comparing the DT methods demonstrated that J-48 (F-score of 
      0.816 and AUC of 0.845) had the best performance. Also, the J-48 (F-score = 80.9% 
      and AUC = 0.822) gained the best performance in generalizability using the 
      external dataset. CONCLUSIONS: The study results demonstrated that DT algorithms 
      can be used to predict ICU admission requirements in COVID-19 patients based on 
      the first time of admission data. Implementing such models has the potential to 
      inform clinicians and managers to adopt the best policy and get prepare during 
      the COVID-19 time-sensitive and resource-constrained situation.
CI  - (c) 2022 Published by Elsevier Ltd.
FAU - Shanbehzadeh, Mostafa
AU  - Shanbehzadeh M
AD  - Department of Health Information Technology, School of Paramedical, Ilam 
      University of Medical Sciences, Ilam, Iran.
FAU - Nopour, Raoof
AU  - Nopour R
AD  - Department of Health Information Management, Student Research Committee, School 
      of Health Management and Information Sciences Branch, Iran University of Medical 
      Sciences, Tehran, Iran.
FAU - Kazemi-Arpanahi, Hadi
AU  - Kazemi-Arpanahi H
AD  - Department of Health Information Technology, Abadan University of Medical 
      Sciences, Abadan, Iran.
AD  - Department of Student Research Committee, Abadan University of Medical Sciences, 
      Iran.
LA  - eng
PT  - Journal Article
DEP - 20220318
PL  - England
TA  - Inform Med Unlocked
JT  - Informatics in medicine unlocked
JID - 101718051
PMC - PMC8930186
OTO - NOTNLM
OT  - COVID-19
OT  - Coronavirus
OT  - Decision tree
OT  - Intensive care unit
OT  - Machine learning
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2022/03/24 06:00
MHDA- 2022/03/24 06:01
PMCR- 2022/03/18
CRDT- 2022/03/23 05:10
PHST- 2022/01/22 00:00 [received]
PHST- 2022/02/25 00:00 [revised]
PHST- 2022/03/15 00:00 [accepted]
PHST- 2022/03/23 05:10 [entrez]
PHST- 2022/03/24 06:00 [pubmed]
PHST- 2022/03/24 06:01 [medline]
PHST- 2022/03/18 00:00 [pmc-release]
AID - S2352-9148(22)00068-5 [pii]
AID - 100919 [pii]
AID - 10.1016/j.imu.2022.100919 [doi]
PST - ppublish
SO  - Inform Med Unlocked. 2022;30:100919. doi: 10.1016/j.imu.2022.100919. Epub 2022 
      Mar 18.