Treatment Outcome Prediction

Treatment outcome prediction aims to forecast the success or failure of a medical intervention using patient data, ultimately personalizing treatment and improving healthcare efficiency. Current research heavily utilizes machine learning, particularly deep learning architectures like convolutional neural networks and recurrent neural networks, often incorporating both imaging and tabular data to build predictive models. These models show promise across various diseases, from cancer and depression to diabetic retinopathy, but challenges remain in external validation and addressing biases inherent in observational data. Successful implementation could significantly improve patient care by optimizing treatment selection and resource allocation.