Early Onset

Early onset prediction focuses on anticipating the initiation of various conditions, from diseases like Alzheimer's and sepsis to the onset of acute kidney injury or even the signaling behavior preceding epileptic seizures. Research heavily utilizes machine learning, employing models such as XGBoost, deep learning architectures (including convolutional neural networks and LSTMs), and dynamic Bayesian networks to analyze diverse data sources, including electronic health records, sensor data, and medical images. Accurate early prediction holds significant promise for improving patient outcomes through timely interventions and personalized treatment strategies, as well as enhancing the efficiency and effectiveness of clinical trials.