Machine Learning

Machine learning is framed as promising for medication-error detection because it can learn from complex and multimodal datasets. Recursive feature elimination is intended to reduce overfitting and improve interpretability. Machine learning may capture patterns that traditional rule-based or statistical approaches miss in complex healthcare data. Deep neural networks are implemented alongside conventional models to compare performance and interpretability. Modern AI is claimed to handle bed-sensor complexity better than earlier rules-based methods. Machine learning can flexibly fit complex data and has been used in adult invasive fungal infection prediction. One adult candidaemia study found four machine learning models outperformed logistic regression. No paediatric IFI model in the review used machine learning, possibly because IFI is rare and sample sizes were small. The field needs clinically useful therapies rather than expensive wearable tools without clinical value. Machine learning is needed because bed-sensor data include movement, partner motion, covers, and environmental artifacts. Machine learning is presented as the layer that turns noisy sleep biosignals into usable…