Objective To update previous systematic review of predictive models for 28-day or 30-day
unplanned hospital readmissions. Design Systematic review. Setting/data source CINAHL …

Objectives Hospital readmission risk prediction facilitates the identification of patients
potentially at high risk so that resources can be used more efficiently in terms of cost-benefit …

Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate
black-box functions. The massive computational capability of edge devices such as mobile …

Risk sharing arrangements between hospitals and payers together with penalties imposed
by the Centers for Medicare and Medicaid (CMS) are driving an interest in decreasing early …

Bayesian optimization (BO) has recently been extended to the federated learning (FL)
setting by the federated Thompson sampling (FTS) algorithm, which has promising …

Background and objective In healthcare systems, the cost of unplanned readmission
accounts for a large proportion of total hospital payment. Hospital-specific readmission rate …

This study intends to predict in-hospital and 6-month mortality, as well as 30-day and 90-day
hospital readmission, using Machine Learning (ML) approach via conventional features. A …

One of the major challenges that confront medical experts during a pandemic is the time
required to identify and validate the risk factors of the novel disease and to develop an …

Background Hospital readmissions are a key quality metric, which has been tied to
reimbursement. One strategy to reduce readmissions is to direct resources to patients at the …

Readmissions among Medicare beneficiaries are a major problem for the US healthcare
system from a perspective of both healthcare operations and patient caregiving outcomes …