158 lines
4.7 KiB
Python
158 lines
4.7 KiB
Python
from __future__ import annotations
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import csv
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import re
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from pathlib import Path
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LABELS = Path("labels.csv")
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OUT = Path("organ_involvement_label_mapping.csv")
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ORGANS = [
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"brain_neurologic",
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"heart",
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"artery_vascular",
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"immune",
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"intestine_digestive",
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"kidney",
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"liver",
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"lung",
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"muscle_musculoskeletal",
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"pancreas_endocrine",
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"adipose_metabolic",
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"female_reproductive",
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"male_reproductive",
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"neoplasm",
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]
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def _code_key(code: str) -> tuple[str, int, str]:
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code = code.strip().upper()
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match = re.match(r"^([A-Z])(\d{2})(?:\.?([A-Z0-9]+))?", code)
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if not match:
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raise ValueError(f"Invalid ICD-10 code: {code!r}")
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letter, number, suffix = match.groups()
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return letter, int(number), suffix or ""
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def _in_range(code: str, start: str, end: str) -> bool:
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c_letter, c_num, _ = _code_key(code)
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s_letter, s_num, _ = _code_key(start)
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e_letter, e_num, _ = _code_key(end)
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if s_letter == e_letter:
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return c_letter == s_letter and s_num <= c_num <= e_num
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return (
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(s_letter < c_letter < e_letter)
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or (c_letter == s_letter and c_num >= s_num)
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or (c_letter == e_letter and c_num <= e_num)
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)
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def _matches_any(code: str, ranges: list[tuple[str, str]]) -> bool:
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return any(_in_range(code, start, end) for start, end in ranges)
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def organ_for_icd10(code: str) -> tuple[str, str]:
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code = code.strip().upper()
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if not re.match(r"^[A-Z]\d{2}", code):
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return "", "unmapped_non_icd10"
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if _matches_any(code, [("C00", "D48")]):
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return "neoplasm", "neoplasm_c00_d48"
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if _matches_any(code, [("F00", "F09"), ("G00", "G99"), ("I60", "I69")]):
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return "brain_neurologic", "f00_f09_g00_g99_i60_i69"
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if _matches_any(code, [("I00", "I09"), ("I20", "I52")]):
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return "heart", "i00_i09_i20_i52"
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if _matches_any(code, [("I10", "I15"), ("I70", "I89"), ("I95", "I99")]):
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return "artery_vascular", "i10_i15_i70_i89_i95_i99"
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if _matches_any(code, [("A00", "B99"), ("D50", "D89")]):
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return "immune", "a00_b99_d50_d89"
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if _matches_any(code, [("J00", "J99")]):
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return "lung", "j00_j99"
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if _matches_any(code, [("K70", "K77")]):
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return "liver", "k70_k77"
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if _matches_any(code, [("K85", "K86"), ("E10", "E16")]):
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return "pancreas_endocrine", "k85_k86_e10_e16"
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if _matches_any(code, [("K00", "K69"), ("K78", "K84"), ("K87", "K93")]):
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return "intestine_digestive", "k00_k69_k78_k84_k87_k93"
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if _matches_any(code, [("N00", "N39")]):
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return "kidney", "n00_n39"
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if _matches_any(code, [("N70", "N98"), ("O00", "O99")]):
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return "female_reproductive", "n70_n98_o00_o99"
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if _matches_any(code, [("N40", "N53")]):
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return "male_reproductive", "n40_n53"
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if _matches_any(code, [("M00", "M99")]):
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return "muscle_musculoskeletal", "m00_m99"
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if _matches_any(code, [("E00", "E09"), ("E17", "E90")]):
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return "adipose_metabolic", "e00_e09_e17_e90"
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return "", "unmapped_no_organ_rule"
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def main() -> None:
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rows = []
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with LABELS.open(encoding="utf-8") as f:
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for i, line in enumerate(f):
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line = line.strip()
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if not line:
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continue
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parts = line.split(maxsplit=1)
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code = parts[0].strip().upper()
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name = parts[1].strip() if len(parts) > 1 else ""
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organ_id, match_source = organ_for_icd10(code)
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rows.append(
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{
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"token_id": i + 3,
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"label_code": code,
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"label_name": name,
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"organ_id": organ_id,
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"organ_label": organ_id,
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"organ_weight": 1.0 if organ_id else 0.0,
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"match_source": match_source,
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"mapping_source": (
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"organ-age-inspired clinical systems based on "
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"Oh et al. Nature 2023; single-label ICD-10 rules"
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),
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}
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)
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fieldnames = [
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"token_id",
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"label_code",
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"label_name",
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"organ_id",
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"organ_label",
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"organ_weight",
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"match_source",
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"mapping_source",
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]
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with OUT.open("w", newline="", encoding="utf-8-sig") as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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writer.writerows(rows)
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mapped = [row for row in rows if row["organ_id"]]
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print(f"labels: {len(rows)}")
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print(f"mapped_labels: {len(mapped)}")
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print(f"unmapped_labels: {len(rows) - len(mapped)}")
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print(f"organs: {', '.join(ORGANS)}")
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print(f"output: {OUT}")
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if __name__ == "__main__":
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main()
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