Segmenting clinical sections

A clinical note is not flat text, it is a sequence of named sections (Chief Complaint, HPI, Past Medical History, Medications, Allergies, Assessment & Plan). The same phrase means different things in different sections: "diabetes" in PMH is historical context, "diabetes" in Assessment & Plan is an active problem, and "penicillin" under Allergies is an adverse-reaction flag, not a current medication. Splitting the note into canonical sections before NER or de-identification gives every downstream OpenMed step the context it needs to be more precise, and lets you process sensitive sections under stricter policies.

When to use

• You have a free-text note, H&P, progress note, or discharge summary and are about to run NER (extracting-clinical-entities) or de-identification.
• The user wants section detection, header parsing, LOINC section mapping, or per-section processing (e.g. "redact the Social History section harder").
• Downstream NER is over- or under-firing because it can't tell historical PMH mentions from active A&P problems.

Quick start

import re
import openmed

# Synthetic note.
note = """CHIEF COMPLAINT: chest pain.
HPI: 54M with 2 hours of substernal pressure.
PAST MEDICAL HISTORY: type 2 diabetes, prior MI 2019.
MEDICATIONS: metformin 500 mg BID.
ALLERGIES: penicillin (rash).
ASSESSMENT AND PLAN: acute coronary syndrome; start aspirin, admit."""

# Map common header variants -> canonical section + LOINC document-section code.
SECTION_MAP = {
    "chief complaint": ("Chief Complaint", "10154-3"),
    "hpi": ("History of Present Illness", "10164-2"),
    "history of present illness": ("History of Present Illness", "10164-2"),
    "past medical history": ("Past Medical History", "11348-0"),
    "medications": ("Medications", "10160-0"),
    "allergies": ("Allergies", "48765-2"),
    "assessment and plan": ("Assessment and Plan", "51847-2"),
}