Antenatal prediction models for outcomes of extremely and very preterm infants based on machine learning

Ushida, Takafumi; Kotani, Tomomi; Baba, Joji; Imai, Kenji; Moriyama, Yoshinori; Nakano‐Kobayashi, Tomoko; Iitani, Yukako; Nakamura, Noriyuki; Hayakawa, Masahiro; Kajiyama, Hiroaki

doi:10.1007/s00404-022-06865-x

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Causal analysis is based on the Shapley value approach, which is used to evaluate the contribution of each modelling factor to the predictive power of the model, and was initially used to address the conflicting distribution of benefits in a multi-person collaborative process [9] . In addition, Shapley value is a locally interpretable framework for analysing the contribution of each risk factor to the model's outcome when making risk assessment results for pregnant women, which can be used to guide clinical diagnosis and treatment [10] .…”

Section: ) Analysis Of the Causes Of Adverse Pregnancy Outcomesmentioning

confidence: 99%

Distributed computing and artificial intelligence-based clinical decision support system for adverse pregnancy outcomes

Luo,

Zhang,

Yuan

2023

Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023)

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Section: ) Analysis Of the Causes Of Adverse Pregnancy Outcomesmentioning

confidence: 99%

Distributed computing and artificial intelligence-based clinical decision support system for adverse pregnancy outcomes

Luo,

Zhang,

Yuan

2023

Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023)

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“…The most known used method for prediction in clinical practice is the scoring system based on logistic regression analysis (14)(15)(16). But as a classic statistic model, logistic regression-based methods may be insufficient in making full use of risk factor information (17)(18)(19)(20). The emergence of ML (machine learning) has brought a turning point for improving this problem.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a risk prediction model for cage subsidence after instrumented posterior lumbar fusion based on machine learning: a retrospective observational cohort study

et al. 2023

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BackgroundInterbody cage subsidence is a common complication after instrumented posterior lumbar fusion surgery, several previous studies have shown that cage subsidence is related to multiple factors. But the current research has not combined these factors to predict the subsidence, there is a lack of an individualized and comprehensive evaluation of the risk of cage subsidence following the surgery. So we attempt to identify potential risk factors and develop a risk prediction model that can predict the possibility of subsidence by providing a Cage Subsidence Score (CSS) after surgery, and evaluate whether machine learning-related techniques can effectively predict the subsidence.MethodsThis study reviewed 59 patients who underwent posterior lumbar fusion in our hospital from 2014 to 2019. They were divided into a subsidence group and a non-subsidence group according to whether the interbody fusion cage subsidence occurred during follow-up. Data were collected on the patient, including age, sex, cage segment, number of fusion segments, preoperative space height, postoperative space height, preoperative L4 lordosis Angle, postoperative L4 lordosis Angle, preoperative L5 lordosis Angle, postoperative PT, postoperative SS, postoperative PI. The conventional statistical analysis method was used to find potential risk factors that can lead to subsidence, then the results were incorporated into stepwise regression and machine learning algorithms, respectively, to build a model that could predict the subsidence. Finally the diagnostic efficiency of prediction is verified.ResultsUnivariate analysis showed significant differences in pre−/postoperative intervertebral disc height, postoperative L4 segment lordosis, postoperative PT, and postoperative SS between the subsidence group and the non-subsidence group (p < 0.05). The CSS was trained by stepwise regression: 2 points for postoperative disc height > 14.68 mm, 3 points for postoperative L4 segment lordosis angle >16.91°, and 4 points for postoperative PT > 22.69°. If the total score is larger than 0.5, it is the high-risk subsidence group, while less than 0.5 is low-risk. The score obtains the area under the curve (AUC) of 0.857 and 0.806 in the development and validation set, respectively. The AUC of the GBM model based on the machine learning algorithm to predict the risk in the training set is 0.971 and the validation set is 0.889. The AUC of the avNNet model reached 0.931 in the training set and 0.868 in the validation set, respectively.ConclusionThe machine learning algorithm has advantages in some indicators, and we have preliminarily established a CSS that can predict the risk of postoperative subsidence after lumbar fusion and confirmed the important application prospect of machine learning in solving practical clinical problems.

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Machine learning model‐based preterm birth prediction and clinical nomogram: A big retrospective cohort study

Liu,

Shen

2024

Intl J Gynecology & Obste

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ObjectiveThis study sought to develop a multifactorial predictive model for preterm birth risk, with the goal of providing clinical practitioners with early prevention.MethodsThis retrospective cohort study utilized 2022 and 2018 National Vital Statistics System (NVSS) birth data, with the 2022 cohort arbitrarily split into training (70%) and internal verification (30%) subsets, and the 2018 cohort for external validation. Four machine learning algorithms—logistic regression, adaptive lasso regression, bootstrap forest, and boosted trees—identified features associated with preterm birth. The study then integrated the consensus features identified across the four models to construct a logistic regression‐based preterm birth prediction nomogram. To evaluate the model's efficacy, calibration, receiver operating characteristic (ROC), and decision curve analysis were applied to both the internal and external validation sets.ResultsThe study included 2 567 040 mother–infant pairs from the 2022 cohort and 2 688 568 mother–infant pairs from the 2018 cohort. All four machine learning models demonstrated high accuracy (area under the curve [AUC] >0.7) in predicting preterm birth, and the internal validation results indicated good model generalizability. Feature selection identified nine common risk factors associated with preterm birth. The prediction nomogram based on these nine common features achieved AUCs of 0.701, 0.702, and 0.704 in the training, internal validation, and external validation sets, respectively. The calibration curves showed good agreement, and the decision curve analysis confirmed the model's net clinical benefits.ConclusionThis study developed a reliable preterm birth prediction tool using large‐scale birth cohort data, filling the gap of lacking external validation for existing preterm birth prediction models.

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Antenatal prediction models for outcomes of extremely and very preterm infants based on machine learning

Cited by 4 publications

References 30 publications

Distributed computing and artificial intelligence-based clinical decision support system for adverse pregnancy outcomes

Distributed computing and artificial intelligence-based clinical decision support system for adverse pregnancy outcomes

Development and validation of a risk prediction model for cage subsidence after instrumented posterior lumbar fusion based on machine learning: a retrospective observational cohort study

Machine learning model‐based preterm birth prediction and clinical nomogram: A big retrospective cohort study

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