System to identifies syntactic elements and medical concepts within clinical text using NLP. phase 2

To achieve the structural architecture and solution depicted in the two images — leading up to medical code assignment — a multi-step NLP + Knowledge Graph + Rule Engine + AI pipeline can be used. Here's how it can be built and the components involved:

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🔹 Step 1: Semantic Representation & Syntactic Analysis (Image 1)

Goal: Identify syntactic elements (e.g., subject, verb) and extract medical concepts from clinical text.

🔧 Components:

1. Text Preprocessing:

   • Tokenization

   • Sentence segmentation

   • POS tagging and lemmatization

2. Dependency Parsing & Named Entity Recognition (NER):

   • Use clinical NLP models (e.g., spaCy, Stanza, cTAKES, or BioBERT) to extract entities like:

     • Patient

     • Symptoms (e.g., chest pain)

     • Conditions (e.g., hypertension)

     • Negated concepts (e.g., “denies vomiting”)

3. Assertion & Negation Detection:

   • Use assertion classifiers to check whether a medical concept is present, negated, hypothetical, etc.

4. Temporal Context Handling:

   • Identify past vs. current conditions (e.g., “history of hypertension” vs. “presenting with chest pain”).

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🔹 Step 2: Ontological Linking & Narrative Construction (Image 2)

Goal: Contextualize extracted medical terms using ontologies, then assemble a clinically meaningful narrative.

🔧 Components:

1. Ontology Integration:

   • Map concepts to clinical ontologies like:

     • SNOMED CT

     • ICD-10-CM

     • UMLS

   • Example: “chest pain” → Angina Pectoris in SNOMED CT

2. Knowledge Graph Construction:

   • Use graph-based structures to link:

     • Symptoms → Conditions

     • Diagnosed by → Procedures

     • Treated by → Medications

3. Contextual Understanding (Narrative Construction):

   • Use language models fine-tuned on clinical texts (e.g., ClinicalBERT, GatorTron) to:

     • Understand causality (e.g., aspirin used for heart attack prevention)

     • Link related concepts across the document

     • Form clinical summaries

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🔹 Final Step: Medical Code Assignment

Goal: Assign the correct diagnosis/procedure codes based on the structured clinical narrative.

🔧 Components:

1. Code Mapping Engine:

   • Rules-based + ML hybrid system to match narrative segments to appropriate codes (e.g., ICD-10, HCC, CPT).

2. Confidence Scoring:

   • Each code assigned a confidence score based on the evidence strength in the document.

3. Human-in-the-loop Review (Optional):

   • Highlight uncertain predictions or low-confidence cases for human coders to validate.

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🛠️ Tools & Frameworks You Can Use:

• NLP: spaCy + scispaCy, Hugging Face Transformers, AllenNLP, cTAKES

• Ontologies: SNOMED CT, UMLS, RxNorm

• Graph DB: Neo4j, RDF triple stores

• ML/AI Models: BioBERT, ClinicalBERT, custom LSTM/Transformer models

• Code Assignment: Rule-based logic + XGBoost/Random Forest/LLM for mapping

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Chat GPT can give a diagram or flowchart version of this pipeline.