The study focuses on extracting medical entities—symptoms and conditions (SCs) and drugs and treatments (DTs)—from patient-authored text (PAT) using lexico-syntactic patterns learned from seed dictionaries. Existing tools have struggled with accuracy in identifying these entities in PAT. The proposed system improves on previous methods by identifying terms not present in initial dictionaries, with notable performance improvements over established tools like MetaMap and OBA. The extraction method achieved F1 scores of 58-70% for DT terms and 66-76% for SC terms across MedHelp forums, demonstrating its efficacy in handling informal terms and providing a novel approach to analyzing PAT.