PMID- 33633935
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220420
IS  - 2224-4344 (Print)
IS  - 2224-4344 (Electronic)
IS  - 2224-4336 (Linking)
VI  - 10
IP  - 1
DP  - 2021 Jan
TI  - Predicting the postoperative blood coagulation state of children with congenital 
      heart disease by machine learning based on real-world data.
PG  - 33-43
LID - 10.21037/tp-20-238 [doi]
AB  - BACKGROUND: Postoperative blood coagulation assessment of children with 
      congenital heart disease (CHD) has been developed using a conventional 
      statistical approach. In this study, the machine learning (ML) was used to 
      predict postoperative blood coagulation function of children with CHD, and assess 
      an array of ML models. METHODS: This was a retrospective and data mining study. 
      Based on the samples of 1,690 children with CHD, and screening data based on 
      demographic characteristics, conventional coagulation tests (CCTs) and complete 
      blood count (CBC), with a precise data selection process, and the support of data 
      mining and ML algorithms including Decision tree, Naive Bayes, Support Vector 
      Machine (SVM), Adaptive Boost (AdaBoost) and Random Forest model, and explored 
      the best prediction models of postoperative blood coagulation function for 
      children with CHD by models performance measured in the area under the receiver 
      operating characteristic (ROC) curve (AUC), calibration or Lift curves, and 
      further verified the reliability of the models with statistical tests. RESULTS: 
      In primary objective prediction, as decision tree, Naive Bayes, SVM, the AUC of 
      our prediction algorithm was 0.81, 0.82, 0.82, respectively. The accuracy rate of 
      the overall forecast has reached more than 75%. Subsequently, we furtherly build 
      improved models. Among them, the true positive rate of the AdaBoost, Random 
      Forest and SVM prediction models reached more than 80% in the ROC curve. These 
      overall accuracy rate indicated a good classification model. Combined calibration 
      curves and Lift curves, the better fit is the SVM model, which predicted 
      postoperative abnormal coagulation, Lift =2.2, postoperative normal coagulation, 
      Lift =1.8. The statistical results furtherly proved the reliability of ML models. 
      The age, sex, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), 
      mean corpuscular hemoglobin concentration (MCHC), white blood cell count (WBC) 
      and platelet count (PLT) were the key features for predicting the postoperative 
      blood coagulation state of children with CHD. CONCLUSIONS: ML technology and data 
      mining algorithms may be used for outcome prediction in children with CHD for 
      postoperative blood coagulation state based on the bulk of clinical data, 
      especially CBC indictors from the real world.
CI  - 2021 Translational Pediatrics. All rights reserved.
FAU - Guo, Kai
AU  - Guo K
AD  - Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical 
      University, National Center for Children's Health, Beijing, China.
FAU - Fu, Xiaoyan
AU  - Fu X
AD  - Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical 
      University, National Center for Children's Health, Beijing, China.
FAU - Zhang, Huimin
AU  - Zhang H
AD  - Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical 
      University, National Center for Children's Health, Beijing, China.
FAU - Wang, Mengjian
AU  - Wang M
AD  - Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical 
      University, National Center for Children's Health, Beijing, China.
FAU - Hong, Songlin
AU  - Hong S
AD  - Fane Data Technology Corporation, Tianjin, China.
FAU - Ma, Shuxuan
AU  - Ma S
AD  - Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical 
      University, National Center for Children's Health, Beijing, China.
LA  - eng
PT  - Journal Article
PL  - China
TA  - Transl Pediatr
JT  - Translational pediatrics
JID - 101649179
PMC - PMC7882284
OTO - NOTNLM
OT  - Congenital heart disease (CHD)
OT  - blood coagulation
OT  - children
OT  - machine learning (ML)
OT  - postoperative
COIS- Conflicts of Interest: All authors have completed the ICMJE uniform disclosure 
      form (available at http://dx.doi.org/10.21037/tp-20-238). The authors have no 
      conflicts of interest to declare.
EDAT- 2021/02/27 06:00
MHDA- 2021/02/27 06:01
PMCR- 2021/01/01
CRDT- 2021/02/26 06:04
PHST- 2021/02/26 06:04 [entrez]
PHST- 2021/02/27 06:00 [pubmed]
PHST- 2021/02/27 06:01 [medline]
PHST- 2021/01/01 00:00 [pmc-release]
AID - tp-10-01-33 [pii]
AID - 10.21037/tp-20-238 [doi]
PST - ppublish
SO  - Transl Pediatr. 2021 Jan;10(1):33-43. doi: 10.21037/tp-20-238.