Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Pediatric neurology
Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy
BACKGROUND: Ketogenic diet therapy (KDT) is an established treatment for drug-resistant epilepsy (DRE); however, methods for predicting its effectiveness remain underdeveloped. This study evaluated various machine learning (ML) models in predicting responses to KDT among DRE patients based on electroencephalography data. METHODS: Using leave-one-out cross-validation, this study evaluated 19 ML classifiers in predicting the outcomes of 90 DRE patients based on absolute and relative power, as well as functional connectivity measures (phase-locking value, phase lag index [PLI], and weighted PLI) across standard frequency bands. KDT significantly reduced seizure frequency at three and 6 months after initiation. RESULTS: The most effective classifier at 3 months was a Coarse Tree classifier trained on absolute power (recall = 0.933, precision = 0.767, F2 = 0.894, area under the receiver operating characteristic curve = 0.607). The most effective classifier at 6 months was a Gaussian Naive Bayes classifier trained on weighted PLI + relative power (recall = 0.759, precision = 0.854, F2 = 0.776, area under the receiver operating characteristic curve = 0.603). CONCLUSIONS: This study identified the most effective ML models for predicting KDT outcomes in DRE patients. The results highlight the potential of electroencephalography-based ML tools for guiding KDT treatment in clinical practice.
9 May 2026
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Machine learning-driven flavoromics: Decoding stage-specific volatile compound dynamics and sensory deterioration in stored infant formula
Infant formula (IF) is a vital nutritional source for infants. However, its sensory quality deteriorates during prolonged storage, impacting consumer acceptance. This study investigated the stage-specific sensory deterioration mechanisms and the predictive modeling of IFs during storage by integrating flavoromics, fatty acid dynamics, and machine learning. Quantitative descriptive analysis identified six key attributes driving quality loss, with one-stage IF showing the earliest and most pronounced decline. Volatile compound profiling revealed aldehydes (hexanal, octanal) and ketones as dominant off-flavor markers, correlating with lipid oxidation pathways. Fatty acid analysis showed the degradation of polyunsaturated fatty acids, particularly linoleic and α-linolenic acid, as key drivers of off-flavor generation. Using only six lipid-oxidation-derived volatiles, Random Forest and XGBoost models accurately predicted stage-specific sensory scores (with R2 > 0.85), directly linking fatty acid oxidation to flavor deterioration. This work establishes lipid oxidation as predominant degradation mechanism and provides practical, data-driven tool for shelf-life optimization.
17 Apr 2026
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