NCIt Sentence entity resolver

This advanced pipeline extracts oncological entities from clinical texts and utilizes the sbiobert_base_cased_mli Sentence Bert Embeddings to map these entities to their corresponding National Cancer Institute Thesaurus (NCIt) codes.

**Predicted entities:** 

Adenopathy, Biomarker, Biomarker_Result, Cancer_Dx, Cancer_Score, Cancer_Surgery, Chemotherapy, Cycle_Count, Cycle_Day, Cycle_Number, Direction, Duration, Frequency, Grade, Histological_Type, Hormonal_Therapy, Imaging_Test, Immunotherapy, Invasion, Line_Of_Therapy, Metastasis, Oncogene, Pathology_Result, Pathology_Test, Performance_Status, Radiation_Dose, Radiotherapy, Response_To_Treatment, Route, Site_Bone, Site_Brain, Site_Breast, Site_Liver, Site_Lung, Site_Lymph_Node, Site_Other_Body_Part, Staging, Targeted_Therapy, Tumor_Finding, Unspecific_Therapy

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Utilizing this model significantly enhances the accuracy of cancer-related documentation and research. This precision facilitates standardized data analysis and interoperability across various clinical systems, promoting more consistent and comprehensive cancer care. By streamlining the coding process, the model allows oncologists and researchers to swiftly access and utilize critical data, improving diagnostic and treatment decision-making while reducing administrative overhead.