22,439 research outputs found
Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis
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
Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale
Soil organic carbon (SOC) plays a major role in the global carbon budget. It
can act as a source or a sink of atmospheric carbon, thereby possibly
influencing the course of climate change. Improving the tools that model the
spatial distributions of SOC stocks at national scales is a priority, both for
monitoring changes in SOC and as an input for global carbon cycles studies. In
this paper, we compare and evaluate two recent and promising modelling
approaches. First, we considered several increasingly complex boosted
regression trees (BRT), a convenient and efficient multiple regression model
from the statistical learning field. Further, we considered a robust
geostatistical approach coupled to the BRT models. Testing the different
approaches was performed on the dataset from the French Soil Monitoring
Network, with a consistent cross-validation procedure. We showed that when a
limited number of predictors were included in the BRT model, the standalone BRT
predictions were significantly improved by robust geostatistical modelling of
the residuals. However, when data for several SOC drivers were included, the
standalone BRT model predictions were not significantly improved by
geostatistical modelling. Therefore, in this latter situation, the BRT
predictions might be considered adequate without the need for geostatistical
modelling, provided that i) care is exercised in model fitting and validating,
and ii) the dataset does not allow for modelling of local spatial
autocorrelations, as is the case for many national systematic sampling schemes
On combinatorial optimisation in analysis of protein-protein interaction and protein folding networks
Abstract: Protein-protein interaction networks and protein folding networks represent prominent research topics at the intersection of bioinformatics and network science. In this paper, we present a study of these networks from combinatorial optimisation point of view. Using a combination of classical heuristics and stochastic optimisation techniques, we were able to identify several interesting combinatorial properties of biological networks of the COSIN project. We obtained optimal or near-optimal solutions to maximum clique and chromatic number problems for these networks. We also explore patterns of both non-overlapping and overlapping cliques in these networks. Optimal or near-optimal solutions to partitioning of these networks into non-overlapping cliques and to maximum independent set problem were discovered. Maximal cliques are explored by enumerative techniques. Domination in these networks is briefly studied, too. Applications and extensions of our findings are discussed
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