A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines

Macrohon, Julio Jerison E.; Villavicencio, Charlyn Nayve; Inbaraj, X. Alphonse; Jeng, Jyh-Horng

doi:10.3390/diagnostics12112782

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…In studies about the prediction of miscarriage risk in women with immunologically abnormal pregnancies, various methods have been employed to investigate high-risk factors in pregnant women. However, there are still shortcomings in clinical practice, model performance evaluation, and the practical application of prediction tools, including application complexity ( Bruno et al, 2020 ; Benner et al, 2022 ; Huang et al, 2022 ; Macrohon et al, 2022 ; Hao et al, 2023 ; Luo and Zhou, 2023 ). Additionally, there is a lack of studies conducting retrospective or prospective validation of pregnancy risk prediction models and evaluating the economic aspects of these models.…”

Section: Discussionmentioning

confidence: 99%

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities

Wu,

Yu,

et al. 2024

Front. Pharmacol.

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Introduction: It is known that patients with immune-abnormal co-pregnancies are at a higher risk of adverse pregnancy outcomes. Traditional pregnancy risk management systems have poor prediction abilities for adverse pregnancy outcomes in such patients, with many limitations in clinical application. In this study, we will use machine learning to screen high-risk factors for miscarriage and develop a miscarriage risk prediction model for patients with immune-abnormal pregnancies. This model aims to provide an adjunctive tool for the clinical identification of patients at high risk of miscarriage and to allow for active intervention to reduce adverse pregnancy outcomes.Methods: Patients with immune-abnormal pregnancies attending Sichuan Provincial People’s Hospital were collected through electronic medical records (EMR). The data were divided into a training set and a test set in an 8:2 ratio. Comparisons were made to evaluate the performance of traditional pregnancy risk assessment tools for clinical applications. This analysis involved assessing the cost-benefit of clinical treatment, evaluating the model's performance, and determining its economic value. Data sampling methods, feature screening, and machine learning algorithms were utilized to develop predictive models. These models were internally validated using 10-fold cross-validation for the training set and externally validated using bootstrapping for the test set. Model performance was assessed by the area under the characteristic curve (AUC). Based on the best parameters, a predictive model for miscarriage risk was developed, and the SHapley additive expansion (SHAP) method was used to assess the best model feature contribution.Results: A total of 565 patients were included in this study on machine learning-based models for predicting the risk of miscarriage in patients with immune-abnormal pregnancies. Twenty-eight risk warning models were developed, and the predictive model constructed using XGBoost demonstrated the best performance with an AUC of 0.9209. The SHAP analysis of the best model highlighted the total number of medications, as well as the use of aspirin and low molecular weight heparin, as significant influencing factors. The implementation of the pregnancy risk scoring rules resulted in accuracy, precision, and F1 scores of 0.3009, 0.1663, and 0.2852, respectively. The economic evaluation showed a saving of ¥7,485,865.7 due to the model.Conclusion: The predictive model developed in this study performed well in estimating the risk of miscarriage in patients with immune-abnormal pregnancies. The findings of the model interpretation identified the total number of medications and the use of other medications during pregnancy as key factors in the early warning model for miscarriage risk. This provides an important basis for early risk assessment and intervention in immune-abnormal pregnancies. The predictive model developed in this study demonstrated better risk prediction performance than the Pregnancy Risk Management System (PRMS) and also demonstrated economic value. Therefore, miscarriage risk prediction in patients with immune-abnormal pregnancies may be the most cost-effective management method.

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Section: Discussionmentioning

confidence: 99%

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities

Wu,

Yu,

et al. 2024

Front. Pharmacol.

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“…The application of AI/ML/DL/EL/SSL methods has already shown remarkable performance in other fields of women health care [30][31][32], using different types of data such as clinical data, computed tomography (CT), cardiotocography (CTG), electromyography (EMG), genomic, metabolomic, biophysical, and biochemical data [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

et al. 2023

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Background: IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. Methods: We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. Results: We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80–0.88), specificity = 0.87 (95% CI 0.83–0.90), positive predictive value = 0.78 (95% CI 0.68–0.86), negative predictive value = 0.91 (95% CI 0.86–0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34–49.59). In detail, the RF-SVM (Random Forest–Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. Conclusions: our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.

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“…Doğum sonrası dönemde de hem annenin hem de bebeğin sağlığını izlemek çok önemlidir çünkü her ikisi de ilk dönemde daha yüksek ölüm riski altındadır. Doğum sonrasında yapılan sağlık izlemeleri de semptomların erken tespit edilip tedavi edilmesi açısından önemlidir ve bu da ölüm riskini azaltabilir (Finlayson, Crossland, Bonet, & Downe, 2020;Macrohon, Villavicencio, Inbaraj, & Jeng, 2022;Turban, King, Kobe, Reisner, & Keuroghlian, 2022).…”

Section: Introductionunclassified

“…Bu da her gün yaklaşık 810 kadının hamilelik ve doğumla ilgili önlenebilir nedenlerden dolayı hayatını kaybettiği anlamına gelmektedir. Bu ölümlerin %94'ü, düşük ve orta gelirli ülkelerde yaşayan ergenlik çağındaki gençlerde veya yirmi yaşından küçük ergenlerde gerçekleşmiştir (Macrohon et al, 2022;'WHO', 2019). Hamile kadınlardaki yaş, yüksek veya düşük kan basıncı, kan şekeri seviyeleri, vücut ısısı ve kalp hızı gibi sağlık faktörleri, gebelik sırasında yüksek düzeyde sağlık sorunlarına neden olabilir ve bu da hem kadının hem bebeğin kaybına yol açabilir.…”

Section: Introductionunclassified

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Mühendislik Alanında Akademik Analiz ve Tartışmalar

2023

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Mühendislik bilim, teknoloji ve matematiği birbirine bağlamayı amaçlar ve bir disiplin olarak binlerce yıldır uygulanmaktadır. Mühendisler, hücre zarlarından inşaat ve protezlere, motor ve taşıma verimliliğinin iyileştirilmesine ve yenilenebilir enerji kaynaklarının geliştirilmesine kadar her konuda çalışırlar. Mühendislik, tekerleğin icadına (ve ötesine) kadar uzanıyor olsa da, terimin kendisi, bir "mühendis"in mancınık ve diğer "kuşatma makineleri" gibi askeri motorlar inşa eden kişi anlamına geldiği 14. yüzyıla kadar uzanmaktadır. Mühendislik, askeri uygulamaların ötesinde gelişerek köprüler ve binalar gibi sivil yapılara uygulanmaya başlanmıştır. Bu da onu orijinal askeri mühendislik alanından ayırmak için inşaat mühendisliği teriminin yaratılmasına yol açmıştır. Bu kitapta mühendislik bilimleri incelenmiştir.

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A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines

Cited by 12 publications

References 17 publications

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities

Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

Mühendislik Alanında Akademik Analiz ve Tartışmalar

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