Cross Patient

Cross-patient analysis in healthcare leverages data from multiple individuals to build models applicable across diverse populations, aiming to improve prediction accuracy and generalizability compared to single-patient models. Current research focuses on developing privacy-preserving techniques like federated learning and addressing biases stemming from training data, particularly in applications like blood glucose prediction and disease prevalence estimation. These efforts utilize various machine learning architectures, including large language models and neural networks, to enhance the reliability and trustworthiness of cross-patient models for improved clinical decision-making and personalized medicine. The ultimate goal is to create robust and equitable models that benefit a wider range of patients while safeguarding sensitive health information.