protein structure prediction

**Medical natural language processing (NLP)** uses **AI to extract insights from clinical text** — analyzing physician notes, radiology reports, pathology reports, and medical literature to extract diagnoses, medications, symptoms, and relationships, transforming unstructured clinical narratives into structured, actionable data for research, decision support, and quality improvement. **What Is Medical NLP?** - **Definition**: AI-powered analysis of clinical text and medical documents. - **Input**: Clinical notes, reports, literature, patient communications. - **Output**: Structured data, extracted entities, relationships, insights. - **Goal**: Unlock value in unstructured clinical text (80% of EHR data). **Key Tasks** **Named Entity Recognition (NER)**: - **Task**: Identify medical concepts in text (diseases, drugs, symptoms, procedures). - **Example**: "Patient has type 2 diabetes" → Extract "type 2 diabetes" as disease. - **Use**: Structure clinical notes for analysis, search, decision support. **Relation Extraction**: - **Task**: Identify relationships between entities. - **Example**: "Metformin prescribed for diabetes" → Drug-treats-disease relationship. **Clinical Coding**: - **Task**: Automatically assign ICD-10, CPT codes from clinical notes. - **Benefit**: Reduce coding time, improve accuracy, optimize reimbursement. **Adverse Event Detection**: - **Task**: Identify medication side effects, complications from notes. - **Use**: Pharmacovigilance, safety monitoring. **Phenotyping**: - **Task**: Identify patient cohorts with specific characteristics from EHR. - **Use**: Clinical research, trial recruitment, population health. **Tools & Platforms**: Amazon Comprehend Medical, Google Healthcare NLP, Microsoft Text Analytics for Health, AWS HealthScribe.