Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning

Yu, Lu; Fu, Xianghua; Chen, Fangxiong; Wong, Kelvin Kian Loong

doi:10.1016/j.artmed.2019.101748

Cited by 78 publications

(39 citation statements)

References 35 publications

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“…ML methods were applied to predict babies’ birth weight, using LR [ 122 , 123 ], MLP [ 123 ] and fuzzy logic support vector regression [ 124 ], RF [ 122 ], Bayesian models [ 122 , 125 ] and generalized boosted model [ 122 ]. The studies predict weight using EHR data [ 123 , 124 , 126 ], amniotic fluid [ 125 ], ultrasound images [ 122 ] and CTG traces [ 127 ].…”

Section: Discussionmentioning

confidence: 99%

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Davidson

Boland

2021

Briefings in Bioinformatics

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Objective Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes. Materials and methods We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes. Results We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9). Conclusions Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.

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Section: Discussionmentioning

confidence: 99%

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Davidson

Boland

2021

Briefings in Bioinformatics

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“…Artificial intelligence can allow cases with missing data or outliers to be retained by interpolation and other methods. The larger the number of cases, the more meaningful the statistical results; (2) The XGBoost algorithm is widely used in medicine, and the prediction performance is good [ 40 , 41 ], 3) The model can reconstruct more effective features from the training process of blood transfusion big data, which can be used to predict the blood transfusion volume of patients to make the model have stronger generalization ability and reduce overfitting; (4) Using the difference between the prediction results and the training data for training, with the gradual increase in the data quantity, the accuracy improves in the iterative process, which ensures the incremental learning characteristics of the model; and (5) Currently, doctors are widely used to make blood transfusion decisions by combining various physiological parameters, symptoms and clinical experience. Our research uses a large quantity of historical data as a reference on the basis of doctors’ rich clinical experience, establishes a mathematical model, and adjusts the output of multiple experiments to obtain the best results.…”

Section: Discussionmentioning

confidence: 99%

Intelligent prediction of RBC demand in trauma patients using decision tree methods

Feng

Liu

et al. 2021

Military Med Res

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Background The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors’ experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors. Methods A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC). Results For non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657–0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633–0.751) and the XGBoost (AUC 0.71, 95% CI 0.654–0.756, P < 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893–0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744–0.850) and the CRT (AUC 0.82, 95% CI 0.779–0.853, P < 0.05). Haematocrit (Hct) is an important prediction parameter. Conclusions The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment.

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“…The larger the number of cases, the more meaningful the statistical results. (2) The XGBoost algorithm is widely used in medicine, and the prediction performance is good [40,41]. 3The model can reconstruct more effective features from the training process of blood transfusion big data, which can be used to predict the blood transfusion volume of patients to make the model have stronger generalization ability and reduce over tting.…”

Section: Discussionmentioning

confidence: 99%

Intelligent prediction of RBC demand in trauma patients using decision tree methods

Feng

Liu

et al. 2020

Preprint

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Background: The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors' experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm (classification and regression tree, CRT and eXtreme gradient boosting, XGBoost) was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors. Methods: A total of 1,371 trauma patients who were diverted to the emergency department from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for RBC demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC).Results: The studies we performed showed that non-invasive parameters were used to predict blood transfusion. The LR method was the best, with an AUC of 0.72 (95% confidence interval [CI] 0.657-0.775), which was higher than the CRT AUC of 0.69 (95% CI 0.633-0.751) and the XGBoost AUC of 0.71 (95% CI 0.654-0.756) (P<0.05). The trauma site and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893-0.981), which was higher than the LR AUC of 0.80 (95% CI 0.744-0.850) and the CRT AUC of 0.82 (95% CI 0.779-0.853) (P<0.05). Haematocrit/Haemoglobin is an important prediction parameter. Conclusions: The classification performance of the intelligent prediction model constructed by the decision tree algorithm is not inferior to that of the traditional LR method. With the increase in the data quantity, the accuracy of the model improved in the iteration process, and the prediction performance continuously improved, which is conducive to clinical application and wide promotion.

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Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning

Cited by 78 publications

References 35 publications

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Intelligent prediction of RBC demand in trauma patients using decision tree methods

Intelligent prediction of RBC demand in trauma patients using decision tree methods

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