Postoperative Complication

Postoperative complications represent a significant challenge in healthcare, driving research into accurate prediction models to improve patient outcomes and resource allocation. Current research focuses on developing and validating sophisticated machine learning models, including deep learning architectures like transformers and convolutional neural networks, to predict various complications using diverse data sources such as electronic health records, vital signs, medical images, and even clinical notes processed by large language models. These models aim to provide clinicians with timely risk assessments, enabling proactive interventions and potentially reducing mortality and morbidity. Emphasis is also placed on developing explainable AI methods to enhance the transparency and clinical usability of these predictive tools.