Machine learning is applied to a large, pseudonymised dataset of social care records from 27,590 adults in Oxfordshire to demonstrate the feasibility of social care-based risk modelling and suggest new opportunities to improve anticipatory care planning, health management, and resource allocation.
Xiao Gu, Rebecca Nourse, Lei Lu et al.· npj Health Systems· 0 citations
Aim: This study aims to develop and evaluate predictive models capable of identifying patients at risk of 30-day readmission using structured inpatient data.Material and Method: The analysis was conducted on a fully synthetic dataset designed to reflect the complexity of real-world clinical data while ensuring the prot...
Alican Doğan· Bandırma Onyedi Eylül Üniver...· 0 citations
Abstract Despite more than 90% of cases arising in the community, malnutrition is under-recognised in UK primary care. Current screening tools such as the Malnutrition Universal Screening Tool identify malnutrition once it is already established and are inconsistently implemented. As a result, malnutrition is detected...
Pratyasha Saha, James Holmes, Charlotte Davies et al.· Primary Health Care Research...· 0 citations
Despite the model’s relatively good performance for those without hypertension, the positive predictive value was low, and a significant proportion of true cases remained undetected, suggesting tailored approaches to case-finding may be beneficial in ensuring equity across demographic groups.
Gloria Ihenetu, A. Alkhatib, Vesselin Novov et al.· Journal of Medical Internet...· 0 citations
Hospital-acquired infections (HAIs) constitute a critical challenge in intensive care units (ICUs). In Greece, data limitations due to incomplete electronic record implementation hinder advanced risk management. This study aims to compare the predictive performance of machine learning algorithms versus traditional tech...
Vasileios Georgakis, P. Xenos· Algorithms· 0 citations
Objectives: Potentially inappropriate medication (PIM) use remains highly prevalent among older outpatients, yet individualized risk prediction tools are limited. This study aimed to develop and validate a PIM risk prediction model incorporating a novel disease-weighted index. Methods: A multicenter cross-sectional stu...
Zhao-Yan Chen, Cong Zhou, Yong-Fei Dong et al.· Journal of Clinical Medicine· 0 citations
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