Clinical Text Mining Pipeline (Steps 1–5)

🏥 Clinical Text Mining Pipeline (Steps 1–5) #

This document outlines a high-level clinical text mining pipeline using knowledge graphs, NLP, and structured indexing. The goal is to extract, enrich, and analyze clinical concepts from raw EMR text.

🧾 Step 1: Preprocessing Clinical Documents #

Goal: Prepare and normalize clinical notes for processing.
Tools: Text cleaning, sentence segmentation, tokenizer.

# Example: Clean and split into sentences
import re

clinical_note = "Pt c/o chest pain. No signs of pneumonia. History of stroke. Prescribed metformin."
sentences = re.split(r'\.\s*', clinical_note.lower())

🧠 Step 2: Extract Terms Using Knowledge Graph + NLP #

Goal: Identify medical terms using a knowledge graph and remove ambiguous, negated, or contextual mentions.
Tools: Knowledge Graph (e.g., UMLS), NegEx, ConText

# Simulated medical term dictionary (knowledge graph-based)
medical_terms = {"chest pain": "symptom", "pneumonia": "disease", "stroke": "disease", "metformin": "drug"}

# Filtered sentences (simulate negation/context removal)
filtered_mentions = []
for s in sentences:
    if "no " in s or "history of" in s:
        continue
    for term in medical_terms:
        if term in s:
            filtered_mentions.append(term)

🗂️ Step 3: Index Positive, Present Mentions #

Goal: Store structured, filtered term mentions for later search.
Tools: JSON/DB-based indexing, storing patient-term mappings.

indexed_mentions = [
    {"patient_id": 1, "term": "chest pain"},
    {"patient_id": 1, "term": "metformin"},
]

🧭 Step 4: Query-Time Semantic Expansion #

Goal: Expand the user’s query using KG (synonyms, variants, etc.) and disambiguate based on context.
Tools: Knowledge Graph (UMLS), synonym/semantic type lookup, optional filters

query = "stroke"
expanded_terms = ["stroke", "cva", "cerebrovascular accident"]

# Disambiguate (simplified)
def is_valid(term, patient_age, season):
    return not (term == "heatstroke" and patient_age < 18 and season == "summer")

📊 Step 5: Build Patient-Feature Matrix for Analysis #

Goal: Aggregate term mentions per patient for cohort selection and modeling.
Tools: Pandas, matrix construction, temporal tagging

from collections import defaultdict

feature_matrix = defaultdict(lambda: {"stroke_mention": 0})
patient_metadata = {1: {"age": 65, "season": "spring"}}

for mention in indexed_mentions:
    pid = mention["patient_id"]
    term = mention["term"]
    if term in expanded_terms and is_valid(term, patient_metadata[pid]["age"], patient_metadata[pid]["season"]):
        feature_matrix[pid]["stroke_mention"] += 1

print(dict(feature_matrix))

✅ Summary #

Step Goal Tools
1 Clean & tokenize notes Regex, NLP
2 Extract clean medical terms KG, NegEx, filtering
3 Store structured mentions JSON, DB
4 Expand/interpret queries KG, synonyms, disambiguation
5 Analyze for research Patient-feature matrix, Pandas

This modular pipeline separates data preparation from query-time flexibility, making it robust and reusable.