Structured, Multimodal Real-World Data Can Improve Cancer Outcome Prediction
admin@pathologynews.com2025-01-27T15:23:11+00:00Machine learning models are revolutionizing cancer outcome predictions by analyzing vast amounts of patient data to identify patterns and insights that traditional methods often miss. However, efforts to build these models are limited by manual extraction of key data elements from unstructured data such as clinical notes and pathology reports. This process is time-consuming, error-prone, and limits scalability.










