clinical-nlp

name

clinical-nlp

description

Process and analyze biomedical text data (clinical notes, radiology reports, PubMed abstracts). Use when: (1) Loading biomedical language models (PubMedBERT, ClinicalBERT, BioBERT, BioGPT) and extracting embeddings, (2) Named entity recognition for medical concepts with scispaCy, (3) UMLS concept linking, (4) Text classification of clinical documents, (5) Extracting structured data from unstructured clinical text (vitals, lab values, findings).

Clinical NLP

Workflow

Processing biomedical text involves these steps:

1. Assess the data -- determine text type, check de-identification status
2. Preprocess -- clean text, segment sections, handle abbreviations
3. Choose a model -- select appropriate biomedical LM for the task
4. Execute the task -- NER, classification, embedding extraction, or information extraction
5. Evaluate -- with appropriate metrics (macro F1 for multi-class, per-label for multi-label)

Decision Tree

What type of text?

• Clinical notes (EHR) → Use ClinicalBERT. Check de-identification first.
• PubMed abstracts / literature → Use PubMedBERT
• General biomedical text → Use BioBERT
• Need text generation → Use BioGPT

What is the task?

• Extract medical entities → scispaCy NER + UMLS linking. See clinical-text.md
• Classify documents → Fine-tune BERT (>500 samples) or few-shot LLM (<500). See text-classification.md
• Extract embeddings for downstream ML → See biomedical-lm.md
• Extract structured data (vitals, labs) → Regex patterns. See clinical-text.md

How large is the labeled dataset?

• Large (>1K samples) → Fine-tune biomedical BERT
• Small (100-1K) → Fine-tune with aggressive regularization
• Very small (<100) → Few-shot LLM prompting

ASK the user before starting:

• What type of text are they working with?
• Is the data de-identified? If PHI is present, advise on de-identification first.
• What is the end goal (classification, NER, embeddings, extraction)?

References

File	Read When
references/biomedical-lm.md	Loading PubMedBERT, ClinicalBERT, BioBERT, BioGPT from HuggingFace; extracting embeddings (CLS, mean pooling); sentence similarity
references/clinical-text.md	Preprocessing clinical notes, section segmentation, scispaCy NER, UMLS linking, extracting vitals/lab values, de-identification
references/text-classification.md	Fine-tuning BERT for classification (HuggingFace Trainer), multi-label classification, label extraction from radiology reports, few-shot LLM