Clinical Data Science

Clinical Data Science #

Core Priority: Retrieval-Augmented Generation (RAG) #

RAG is one of the most in-demand skills in clinical GenAI due to:

  • The need to ground LLMs in real patient data
  • Compliance, privacy, and traceability
  • Applications like:
    • Clinical Question Answering
    • Summarization of EHRs
    • Evidence-based recommendations

Key Tools: #

  • Vector DBs: Vertex AI Search, Pinecone, FAISS
  • LLMs: Gemini, GPT-4, PaLM, Med-PaLM
  • Frameworks: LangChain, LlamaIndex, Vertex Extensions

Other High-Demand Skillsets #

  1. Clinical NLP & Information Extraction

    • Named Entity Recognition (NER)
    • Negation detection
    • Temporal event extraction
    • Tools: scispaCy, MedSpaCy, cTAKES, ClinicalBERT

  2. LLMOps & GenAI Engineering

    • Prompt tracking and versioning
      • Chain-of-Thought reasoning pipelines
      • RAG monitoring and evaluation
      • Tools: LangChain, LangSmith, PromptLayer, Trulens

3. Knowledge Graphs & Ontologies # 

- UMLS, SNOMED, HPO integration
    - Graph-based document ranking
    - Symbolic-neural hybrid reasoning
    - **Tools**: Neo4j, BioPortal APIs, KG-BERT

4. Temporal Modeling & Phenotyping # 

- Patient timeline extraction
    - Longitudinal modeling
    - Conversion to OMOP/FHIR representations
    - **Tools**: PyOMOP, Synthea, FHIR parsers

5. Multimodal Clinical AI # 

- OCR and document understanding
    - Fusion of tables, images, and text
    - Radiology + Report generation
    - **Tools**: Document AI (GCP), Form Recognizer (Azure), BioGPT-Vision