/
compute_cpt_baseline.py
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/
compute_cpt_baseline.py
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from collections import Counter
from collections import defaultdict
import csv
import pickle
import re
import typing as t
import requests as rq
from umls_api_auth import Authentication
import numpy as np
from tqdm import tqdm
import pandas as pd
problem_snomed, problem_icd10, problem_icd9 = defaultdict(list), defaultdict(list), defaultdict(list)
print("loading problem definitions")
with open('data/problem_codes_all.csv') as f:
r = csv.reader(f)
header = next(r)
for row in r:
if row[1] == 'icd10':
problem_icd10[row[0]].append(row[2])
if row[1] == 'icd9':
problem_icd9[row[0]].append(row[2])
if row[1] == 'snomed':
problem_snomed[row[0]].append(row[2])
CPT_TO_DISCIPLINE = {
# surgeries
"1010115": "Audiology", # "Surgical Procedures on the Auditory System"
"1006056": "Circulatory", # "Surgical Procedures on the Cardiovascular System"
"1006964": "Gastroenterology", # "Surgical Procedures on the Digestive System"
"1009024": "Endocrinology", # "Surgical Procedures on the Endocrine System"
"1009727": "Ophthalmology", # "Surgical Procedures on the Eye and Ocular Adnexa"
"1008681": "Genitourinary", # "Surgical Procedures on the Female Genital System"
"1006843": "Hematology", # "Surgical Procedures on the Hemic and Lymphatic Systems"
"1003148": "Dermatology", # "Surgical Procedures on the Integumentary System"
"1008470": "Genitourinary", # "Surgical Procedures on the Male Genital System"
"1006933": "Respiratory", # "Surgical Procedures on the Mediastinum and Diaphragm"
"1003679": "Musculoskeletal", # "Surgical Procedures on the Musculoskeletal System"
"1009068": "Neurology", # "Surgical Procedures on the Nervous System"
"1005690": "Respiratory", # "Surgical Procedures on the Respiratory System"
"1008061": "Genitourinary", # "Surgical Procedures on the Urinary System"
# radiology procedures
"1015091": "Musculoskeletal", # "Bone/Joint Studies"
"1010334": "Respiratory", # Diagnostic Radiology (Diagnostic Imaging) Procedures of the Chest
"1010537": "Gastroenterology", # Diagnostic Radiology (Diagnostic Imaging) Procedures of the Gastrointestinal Tract
"1010594": "Circulatory", # Diagnostic Radiology (Diagnostic Imaging) Procedures of the Heart
"1010602": "Circulatory", # Diagnostic Radiology (Diagnostic Imaging) Procedures of the Vascular System
"1010574": "Genitourinary", # Diagnostic Radiology (Diagnostic Imaging) Procedures of the Urinary Tract
"1015090": "Genitourinary", # Breast, Mammography
"1010843": "Oncology", # Radiation Oncology Treatment
# medicine services and procedures
"1013263": "Immunology", # Allergy and Clinical Immunology Procedures
"1012974": "Circulatory", # Cardiovascular Procedures
"1013417": "Mental Health", # Central Nervous System Assessments/Tests (eg, Neuro-Cognitive, Mental Status, Speech Testing)
"1013565": "Musculoskeletal", # Chiropractic Manipulative Treatment Procedures
"1012740": "Genitourinary", # Dialysis Services and Procedures
"1013306": "Endocrinology", # Endocrinology Services
"1012764": "Gastroenterology", # Gastroenterology Procedures
"1013309": "Neurology", # Neurology and Neuromuscular Procedures
"1012793": "Ophthalmology", # Ophthalmology Services and Procedures
"1013483": "Musculoskeletal", # Physical Medicine and Rehabilitation Evaluations
"1012681": "Mental Health", # Psychiatry Services and Procedures
"1013214": "Respiratory", # Pulmonary Procedures
"1013471": "Dermatology", # Special Dermatological Procedures
}
class ICDPrefix(t.NamedTuple):
lowLetter: str
lowNumber: int
highLetter: str
highNumber: int
ICD_10_PREF_TO_DISCIPLINE = {
ICDPrefix("A", 0, "B", 99): "Infectious Disease",
ICDPrefix("C", 0, "D", 49): "Oncology",
ICDPrefix("D", 0, "D", 89): "Hematology",
ICDPrefix("E", 0, "E", 89): "Endocrinology",
ICDPrefix("F", 1, "F", 99): "Mental Health",
ICDPrefix("G", 0, "G", 99): "Neurology",
ICDPrefix("H", 0, "H", 59): "Ophthalmology",
ICDPrefix("H", 60, "H", 95): "Otology",
ICDPrefix("I", 00, "I", 99): "Circulatory",
ICDPrefix("J", 0, "J", 99): "Respiratory",
ICDPrefix("K", 0, "K", 14): "Dentistry",
ICDPrefix("K", 20, "K", 95): "Gastroenterology",
ICDPrefix("L", 0, "L", 99): "Dermatology",
ICDPrefix("M", 0, "M", 99): "Musculoskeletal",
ICDPrefix("N", 0, "N", 99): "Genitourinary",
ICDPrefix("R", 0, "R", 9): "Circulatory",
ICDPrefix("R", 10, "R", 19): "Gastroenterology",
ICDPrefix("R", 20, "R", 23): "Dermatology",
ICDPrefix("R", 25, "R", 29): "Musculoskeletal",
ICDPrefix("R", 30, "R", 39): "Genitourinary",
ICDPrefix("R", 40, "R", 46): "Mental Health",
ICDPrefix("S", 0, "T", 98): "External",
ICDPrefix("Z", 0, "Z", 29): "Health Maintenance",
}
ICD_9_PREF_TO_DISCIPLINE = {
ICDPrefix("", 1, "", 139): "Infectious Disease",
ICDPrefix("", 140, "", 239): "Oncology",
ICDPrefix("", 240, "", 279): "Endocrinology",
ICDPrefix("", 280, "", 289): "Hematology",
ICDPrefix("", 290, "", 319): "MentalHealth",
ICDPrefix("", 320, "", 389): "Neurology",
ICDPrefix("", 390, "", 459): "Circulatory",
ICDPrefix("", 460, "", 519): "Respiratory",
ICDPrefix("", 520, "", 529): "Dentistry",
ICDPrefix("", 530, "", 579): "Gastroenterology",
ICDPrefix("", 580, "", 629): "Genitourinary",
ICDPrefix("", 630, "", 679): "Other",
ICDPrefix("", 680, "", 709): "Dermatology",
ICDPrefix("", 710, "", 739): "Musculoskeletal",
ICDPrefix("", 740, "", 759): "Other",
ICDPrefix("", 760, "", 779): "Other",
ICDPrefix("", 780, "", 799): "Other",
ICDPrefix("", 800, "", 999): "External",
ICDPrefix("V", 1, "V", 91): "Health Maintenance",
ICDPrefix("E", 0, "E", 999): "External",
}
def cpt_to_discipline(px_code, tgt):
# get discipline for a CPT code using our manually created map
disciplines = []
# take part of code before modifier
code = px_code.split('.')[0]
route = f'/content/current/source/CPT/{code}/ancestors'
query = {'ticket': auth.getst(tgt)}
anc = rq.get(URI+route, params=query)
if anc.status_code == 200:
disciplines.extend([CPT_TO_DISCIPLINE[a['ui']] for a in anc.json()['result'] if a['ui'] in CPT_TO_DISCIPLINE])
return set(disciplines)
def icd9_to_disc(dx_code):
prefix = dx_code[:3]
if dx_code[0].isnumeric():
prefix = int(prefix)
for pref, disc in ICD_9_PREF_TO_DISCIPLINE.items():
if prefix_number >= pref.lowNumber and prefix_number <= pref.highNumber:
return disc
else:
prefix_letter = dx_code[0]
# just manually do this
if prefix_letter == "V":
return "Health Maintenance"
elif prefix_letter == "E":
return "External"
return
def icd10_to_disc(dx_code):
prefix = dx_code[:3]
prefix_letter = prefix[0]
prefix_number = int(prefix[1:])
for pref, disc in ICD_10_PREF_TO_DISCIPLINE.items():
if prefix_letter >= pref.lowLetter and prefix_letter <= pref.highLetter and prefix_number >= pref.lowNumber and prefix_number <= pref.highNumber:
return disc
return
def dxs_to_discipline(dx_codes, dx_systems):
# get discipline for ICD code using manually created maps
disciplines = []
for code, system in zip(dx_codes, dx_systems):
if system == "ICD-9-CM":
icd9_disc = icd9_to_disc(code)
disciplines.append(icd9_disc)
elif system == "ICD-10-CM":
icd10_disc = icd10_to_disc(code)
disciplines.append(icd10_disc)
return set(disciplines)
def px_to_prob_match_by_discipline(px, problem):
tgt = auth.gettgt()
px_discs = cpt_to_discipline(px, tgt)
dx_discs = dxs_to_discipline(problem_icd10[problem], ["ICD-10-CM"] * len(problem_icd10[problem]))
return len(px_discs.intersection(dx_discs)) > 0
def is_cpt(code):
return re.match("^[A-Z]\d{4}(\.\d+)?", code) is not None or re.match("^\d{4}[\dFMTU](\.\d+)?$", code) is not None
auth = Authentication('MY-SECRET-KEY')
tgt = auth.gettgt()
URI = "https://uts-ws.nlm.nih.gov/rest"
df = pd.read_csv('data/test_probs.csv')
adf = pd.read_csv('data/all.csv', names=['problem', 'relationType', 'target', 'label'])
procs = sorted([proc.split('_')[1] for proc in adf[adf['relationType'] == 'procedure']['target'].unique()])
print(f"num all procs: {len(set(procs))}")
cpt_procs = [p for p in procs if is_cpt(p)]
print(f"num cpt procs: {len(set(cpt_procs))}")
num_negs = Counter()
num_pos_negs = Counter()
for ix, triple in enumerate(df.itertuples()):
if triple.label == 1:
if triple.relationType != 'procedure':
continue
target = triple.target.split('_')[1]
num_negs[(triple.problem, triple.relationType)] += 1
if is_cpt(target):
if px_to_prob_match_by_discipline(target, triple.problem):
num_pos_negs[(triple.problem, triple.relationType)] += 1
ranks = []
matches = 0
for triple in df.itertuples():
if triple.label != 2 or triple.relationType != 'procedure':
continue
target = triple.target.split('_')[1]
nn = num_negs[(triple.problem, triple.relationType)]
npn = num_pos_negs[(triple.problem, triple.relationType)]
if is_cpt(target):
if px_to_prob_match_by_discipline(target, triple.problem):
rank = npn / 2 + 1
else:
rank = np.median(np.arange(npn+1, nn+1+1))
else:
rank = np.median(np.arange(npn+1, nn+1+1))
ranks.append(rank)
ranks = np.array(ranks)
mr = np.mean(ranks)
mrr = np.mean(1./ranks)
hits_at_1 = np.mean([rank <= 1 for rank in ranks])
hits_at_5 = np.mean([rank <= 5 for rank in ranks])
hits_at_10 = np.mean([rank <= 10 for rank in ranks])
hits_at_30 = np.mean([rank <= 30 for rank in ranks])
print(f"MR: {mr}, MRR: {mrr}, H@1: {hits_at_1}, H@5: {hits_at_5}, H@10: {hits_at_10}, H@30: {hits_at_30}")