clinical-nlp-extractor

$npx mdskill add swaruplab/operon/clinical-nlp-extractor

Extracts medical entities from unstructured clinical text using NLP.

  • Converts free-text clinical notes into structured data for analysis.
  • Uses regex patterns, dictionaries, or LLM wrappers for entity extraction.
  • Tokenizes text and matches against patterns to identify entities.
  • Outputs a JSON list of entities with types and spans.

SKILL.md

.github/skills/clinical-nlp-extractorView on GitHub ↗
---
name: 'clinical-nlp-extractor'
description: 'Extracts medical entities (Diseases, Medications, Procedures) from unstructured clinical text using regex and simple rules (or LLM wrappers).'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
  - read_file
  - run_shell_command
---

# Clinical NLP Entity Extractor

The **Clinical NLP Skill** converts free-text clinical notes into structured data. It identifies key medical entities like problems/diagnoses, medications, and procedures.

## When to Use This Skill

*   When analyzing unstructured EHR notes.
*   To populate a patient's problem list or medication reconciliation.
*   To de-identify text (phi-removal) - *Basic version*.

## Core Capabilities

1.  **NER (Named Entity Recognition)**: Extracts Problems, Drugs, Procedures.
2.  **Negation Detection**: (Basic) Checks if a finding is denied ("No fever").
3.  **Structuring**: Returns JSON format compatible with FHIR/USDL.

## Workflow

1.  **Input**: A string of clinical text or a text file.
2.  **Process**: Tokenizes and matches against patterns/dictionaries.
3.  **Output**: JSON list of entities with spans and types.

## Example Usage

**User**: "Extract entities from this note."

**Agent Action**:
```bash
python3 Skills/Clinical/Clinical_NLP/entity_extractor.py \
    --text "Patient has diabetes type 2. Prescribed Metformin 500mg. No chest pain." \
    --output entities.json
```

```

More from swaruplab/operon

SkillDescription
agentd-drug-discoveryUse the AgentD workflow to mine evidence, design molecules, and rank candidates with SAR plus ADMET annotations for early drug discovery tasks.
alphafold-databaseAccess AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
antibody-design-agentAn advanced agent for de novo antibody design and optimization using state-of-the-art protein language models (MAGE, RFdiffusion).
archrSingle-cell ATAC-seq analysis with ArchR (R). The mature R-based scATAC pipeline — Arrow files, doublet inference, iterative LSI + Harmony, clustering, gene scores, MACS2 peak calling, motif enrichment, chromVAR deviations, footprinting, scRNA-seq integration (label transfer), trajectory analysis (built-in + Monocle3 + Slingshot), and ArchR's interactive genome browser. Sister protocol to snapatac2 — pick this for established R workflows.
automated-bioinformatics-pipelinesPipeline maestro
autonomous-biomedical-agentAutonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
autonomous-oncology-agentPrecision Oncology
basecallingConvert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Note: Guppy is deprecated; use Dorado for all new analyses.
batch-processingProcess multiple sequence files in batch using Biopython. Use when working with many files, merging/splitting sequences, or automating file operations across directories.
bindingdb-databaseQuery BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.