Culture, ethnicity, and socio-economic status as determinants of the management of patients with advanced heart failure who need palliative care: A clinical consensus statement from the Heart Failure Association (HFA) of the ESC, the ESC Patient Forum, and the European Association of Palliative Care.

The delivery of effective healthcare entails the configuration and resourcing of health economies to address the burden of disease, including acute and chronic heart failure, that affects local populations. Increasing migration is leading to more multicultural and ethnically diverse societies worldwide, with migration research suggesting that minority populations are often subject to discrimination, socio-economic disadvantage, and inequity of access to optimal clinical support. Within these contexts, the provision of person-centred care requires medical and nursing staff to be aware of and become adept in navigating the nuances of cultural diversity, and how that can impact some individuals and families entrusted to their care.

Wireless Body Area Network Control Policies for Energy-Efficient Health Monitoring.

Wireless body area networks (WBANs) have strong potential in the field of health monitoring. However, the energy consumption required for accurate monitoring determines the time between battery charges of the wearable sensors, which is a key performance factor (and can be critical in the case of implantable devices). In this paper, we study the inherent trade-off between the power consumption of the sensors and the probability of misclassifying a patient's health state.

Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients.

Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients.