RESUMEN
Heart failure (HF), a worldwide epidemic with significant morbidity and mortality risks, is frequently secondary to cardiovascular disorders and probably is the common final way to survive patients. Almost 25% of hospitalized patients with acute HF are expected to be readmitted within 30 days post-discharge, and the rates of rehospitalization increase to almost one-third at 60 days and 60 percent within one year of discharge. Although care planning for patients with heart failure is complex, multidisciplinary, and resource-dependent, optimal self-care management along with appropriate educational intervention and follow-up strategy could be able to reduce readmissions, decline the duration of hospitalization, increase life expectancy, decrease the rates of mortality, and reduce costs of healthcare services for patients with HF. However, there are contradictions in previous reports about the efficacy of self-care, mainly due to patients' non-adherence to self-care behaviors. Therefore, the current study aimed to review the investigations on the effectiveness of self-care of HF patients in reducing hospital readmissions and increasing quality of life, and discuss novel approaches for predischarge educational interventions and postdischarge follow-up strategies.
RESUMEN
Recent advances in artificial intelligence (AI) have shown great promise in the diagnosis, prediction, treatment plans, and monitoring of neurodegenerative disorders. AI algorithms can analyze huge quantities of data from numerous sources, including medical images, quantifiable proteins in urine, blood, and cerebrospinal fluid (CSF), genetic information, clinical records, electroencephalography (EEG) signals, driving behaviors, and so forth. Alzheimer's disease (AD) is one of the most common neurodegenerative disorders that progressively damage cognitive abilities and memory. This study specifically explores the possible application of AI in the diagnosis, prediction, monitoring, biomarker or drug discovery, and classification of AD.