The application of artificial intelligence (AI) in health care is increasingly relevant, and one field in which it can be especially useful is in hospital management. In particular, the prediction of hospital admissions in patients with congestive heart failure (HF) is an area of great interest, as it can help reduce the burden on both patients and the healthcare system.
A recent study examined 23 studies that used machine learning and deep learning methods to predict hospitalization in patients with HF. Results showed that AI algorithms can successfully predict 30-day hospital (re)admission, with area under the curve (AUC) values ranging from 0,61 to 0,79. It was also shown that the algorithms can predict hospital admission over longer periods of time, from 6 months to 3 years, with an AUC ranging from 0,65 to 0,78.
These results indicate that AI can play a key role in hospital management by accurately predicting hospital admission, enabling clinicians and hospital staff to better plan resources and deliver timely intervention. In addition, the implementation of AI algorithms for the management of HF patients can help reduce the financial burden on the healthcare system by reducing the number of unplanned hospital admissions.
In conclusion, the application of AI in the prediction of hospital admissions in patients with HF has the potential to significantly improve hospital management and reduce the burden on both patients and the healthcare system. It is necessary to continue researching and improving the current models to ensure their clinical applicability in the future.