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MediGuard AI — Medical-Aware Text Chunker
Section-aware chunking with biomarker / condition metadata extraction.
"""
from __future__ import annotations
import re
from dataclasses import dataclass, field
# Biomarker names to detect in chunk text
_BIOMARKER_NAMES: set[str] = {
"Glucose",
"Cholesterol",
"Triglycerides",
"HbA1c",
"LDL",
"HDL",
"Insulin",
"BMI",
"Hemoglobin",
"Platelets",
"WBC",
"RBC",
"Hematocrit",
"MCV",
"MCH",
"MCHC",
"Heart Rate",
"Systolic",
"Diastolic",
"Troponin",
"CRP",
"C-reactive Protein",
"ALT",
"AST",
"Creatinine",
"TSH",
"T3",
"T4",
"Sodium",
"Potassium",
"Calcium",
}
_CONDITION_KEYWORDS: dict[str, str] = {
"diabetes": "diabetes",
"diabetic": "diabetes",
"hyperglycemia": "diabetes",
"insulin resistance": "diabetes",
"anemia": "anemia",
"anaemia": "anemia",
"iron deficiency": "anemia",
"thalassemia": "thalassemia",
"thalassaemia": "thalassemia",
"thrombocytopenia": "thrombocytopenia",
"heart disease": "heart_disease",
"cardiovascular": "heart_disease",
"coronary": "heart_disease",
"hypertension": "heart_disease",
"atherosclerosis": "heart_disease",
"hyperlipidemia": "heart_disease",
}
_SECTION_RE = re.compile(
r"^(?:#+\s*)?("
r"abstract|introduction|background|methods?|methodology|materials?"
r"|results?|findings|discussion|conclusion|summary"
r"|guidelines?|recommendations?|references?|bibliography"
r"|clinical\s*presentation|pathophysiology|diagnosis|treatment|prognosis"
r")\b",
re.IGNORECASE | re.MULTILINE,
)
@dataclass
class MedicalChunk:
"""A single chunk with medical metadata."""
text: str
chunk_index: int
document_id: str = ""
title: str = ""
source_file: str = ""
page_number: int | None = None
section_title: str = ""
biomarkers_mentioned: list[str] = field(default_factory=list)
condition_tags: list[str] = field(default_factory=list)
word_count: int = 0
def to_dict(self) -> dict:
return {
"chunk_text": self.text,
"chunk_index": self.chunk_index,
"document_id": self.document_id,
"title": self.title,
"source_file": self.source_file,
"page_number": self.page_number,
"section_title": self.section_title,
"biomarkers_mentioned": self.biomarkers_mentioned,
"condition_tags": self.condition_tags,
}
class MedicalTextChunker:
"""Section-aware text chunker optimised for medical documents."""
def __init__(
self,
target_words: int = 600,
overlap_words: int = 100,
min_words: int = 50,
):
self.target_words = target_words
self.overlap_words = overlap_words
self.min_words = min_words
def chunk_text(
self,
text: str,
*,
document_id: str = "",
title: str = "",
source_file: str = "",
) -> list[MedicalChunk]:
"""Split text into enriched medical chunks."""
sections = self._split_sections(text)
chunks: list[MedicalChunk] = []
idx = 0
for section_title, section_text in sections:
words = section_text.split()
if not words:
continue
start = 0
while start < len(words):
end = min(start + self.target_words, len(words))
chunk_words = words[start:end]
if len(chunk_words) < self.min_words and chunks:
# merge tiny tail into previous chunk
chunks[-1].text += " " + " ".join(chunk_words)
chunks[-1].word_count = len(chunks[-1].text.split())
break
chunk_text = " ".join(chunk_words)
biomarkers = self._detect_biomarkers(chunk_text)
conditions = self._detect_conditions(chunk_text)
chunks.append(
MedicalChunk(
text=chunk_text,
chunk_index=idx,
document_id=document_id,
title=title,
source_file=source_file,
section_title=section_title,
biomarkers_mentioned=biomarkers,
condition_tags=conditions,
word_count=len(chunk_words),
)
)
idx += 1
start = end - self.overlap_words if end < len(words) else len(words)
return chunks
# ── internal helpers ─────────────────────────────────────────────────
@staticmethod
def _split_sections(text: str) -> list[tuple[str, str]]:
"""Split text by detected section headers."""
matches = list(_SECTION_RE.finditer(text))
if not matches:
return [("", text)]
sections: list[tuple[str, str]] = []
# text before first section header
if matches[0].start() > 0:
preamble = text[: matches[0].start()].strip()
if preamble:
sections.append(("", preamble))
for i, match in enumerate(matches):
header = match.group(1).strip().title()
start = match.end()
end = matches[i + 1].start() if i + 1 < len(matches) else len(text)
body = text[start:end].strip()
# Skip reference/bibliography sections
if header.lower() in ("references", "bibliography"):
continue
if body:
sections.append((header, body))
return sections or [("", text)]
@staticmethod
def _detect_biomarkers(text: str) -> list[str]:
text_lower = text.lower()
return sorted({name for name in _BIOMARKER_NAMES if name.lower() in text_lower})
@staticmethod
def _detect_conditions(text: str) -> list[str]:
text_lower = text.lower()
return sorted({tag for kw, tag in _CONDITION_KEYWORDS.items() if kw in text_lower})
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