Heart Failure

Heart failure, a leading cause of mortality worldwide, is the focus of intense research aimed at improving risk prediction and patient management. Current research emphasizes the development of sophisticated machine learning models, including XGBoost, Random Forests, LSTMs, and large language models (LLMs), often incorporating multi-modal data such as ECGs, echocardiograms, clinical notes, and even voice recordings, to enhance predictive accuracy and clinical interpretability. These advancements hold significant promise for earlier diagnosis, personalized treatment strategies, and improved patient outcomes by enabling more timely and effective interventions. Furthermore, research is exploring methods to address challenges like data imbalance and missing data in clinical datasets to improve the reliability and generalizability of predictive models.