Generalized Biomarker Model for Clinical Trial Matching

The patent applies universality by training a single machine learning model on multiple different biomarkers simultaneously. The model learns to identify various biomarkers (e.g., EGFR, ALK, ROS1) using common document features and patterns, making one model serve multiple functions rather than requiring separate models for each biomarker. This reduces overall system complexity while maintaining identification accuracy across different biomarker types.

The patent merges data from multiple biomarkers into a unified training dataset. By combining documents and features associated with different biomarkers (EGFR, ALK, ROS1, etc.), the model learns shared patterns and features that are common across biomarker documentation. This pooling of data allows the system to achieve accurate identification even for biomarkers with limited individual data, as the model leverages patterns learned from other biomarkers.

The patent replaces manual or traditional systematic searching methods with a machine learning-based automated system. The model processes medical records by learning patterns from training data, automatically identifying patients with specific biomarkers through probabilistic inference rather than exhaustive manual searching. This substitution maintains reliable patient identification while dramatically improving processing efficiency and scalability.

The patent implements self-service by enabling the machine learning model to automatically perform patient identification without requiring manual review. The model processes medical records, applies learned patterns, and generates patient recommendations autonomously. This automation maintains high accuracy in patient selection while eliminating time-consuming manual review processes, allowing the system to scale to large datasets efficiently.