System to identifies syntactic elements and medical concepts within clinical text using NLP. phase 1

 The image illustrates a semantic representation system that identifies syntactic (of or according to syntax) elements and medical concepts within clinical text using NLP. To achieve this structural architecture, we can break it down into components and explore the methods and tools commonly used in similar systems.






✅ Key Functional Blocks & Implementation Options:

1. Text Preprocessing

  • Goal: Clean, normalize, and tokenize the clinical note.

  • Tools: spaCy, NLTK, ScispaCy, MedSpaCy

  • Steps:

    • Sentence segmentation

    • Tokenization

    • Lemmatization

    • Stopword removal (with care in clinical texts)

2. Syntactic Analysis

  • Goal: Parse sentence structure to identify roles (subject, verb, object).

  • Tools/Methods:

    • Dependency Parsing: SpaCy, Stanza, AllenNLP

    • Constituency Parsing: Benepar, CoreNLP

    • Rule-based agent-action mapping (e.g., "patient" → agent of "complains")

3. Named Entity Recognition (NER)

  • Goal: Identify medical entities like diseases, symptoms, procedures, etc.

  • Tools:

    • ScispaCy (trained on biomedical text)

    • MetaMap, QuickUMLS (for clinical concept mapping)

    • Custom NER Models (transformers like BioBERT, ClinicalBERT)

4. Negation and Context Detection

  • Goal: Detect modifiers like "denies vomiting" → negated symptom.

  • Tools:

    • NegEx, ConText, NegBio

    • Clinical BERT models fine-tuned for assertion status

5. Medical Concept Normalization

  • Goal: Map free-text to standardized terms (ICD-10, SNOMED CT, HCC)

  • Tools:

    • QuickUMLS

    • BioPortal APIs

    • cTAKES with dictionary mapping

6. Visualization & Highlighting

  • Goal: Overlay structured data on unstructured text visually

  • Implementation:

    • Frontend: HTML/CSS with JavaScript libraries like D3.js or Chart.js

    • Highlighting: Use span tags with classes to show different concepts

    • Backend: Send NLP output as structured JSON to UI

🔁 End-to-End Pipeline Frameworks:

  • Apache cTAKES

  • CLAMP (Clinical Language Annotation, Modeling, and Processing)

  • MedSpaCy

  • Custom pipelines using Hugging Face Transformers

💡 Architecture Summary:

Raw Clinical Text
       ↓
 Preprocessing
       ↓
Syntactic Parsing ←→ Named Entity Recognition
       ↓                 ↓
Context/Negation      Concept Mapping (UMLS/HCC)
       ↓                 ↓
      Structured Output with Tagging → Visualization Layer

____________

chat gpt can give Python-based architecture demo or sample code showing this in action with BioBERT or ScispaCy.


These are found in platforms like Nym , Fathom ( prominent platforms). There are others like CodaMetrix and AUTOCODE.AI

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