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"""
Code copied from AGXNet:
https://github.com/batmanlab/AGXNet
"""
import argparse
import pandas as pd
from tqdm import tqdm
import spacy
sp = spacy.load("en_core_web_sm")
parser = argparse.ArgumentParser(description="Pharse RadGraph Relations.")
parser.add_argument(
"--input-path",
default="/PROJECT DIR/preprocessing/mimic-cxr-radgraph-itemized.csv",
help="Itemized input data path.",
)
parser.add_argument(
"--output-path",
default="/PROJECT DIR/preprocessing/mimic-cxr-radgraph-sentence-parsed.csv",
help="Output path for parsed relations.",
)
def obs_lemmatization(x):
"""
Lemmatize observation
Args:
x: a observation token
Return:
normalized observation
"""
w_lst = []
for word in sp(str(x)):
w_lst.append(word.lemma_)
return " ".join(w_lst)
def radgraph_parse(args):
"""Pharse RadGraph relations."""
print("Loading itemized RadGraph data...")
df_itemized = pd.read_csv(args.input_path)
# get all study_id
sid_lst = list(df_itemized["study_id"].unique())
tuple_lst = []
print("Preprocessing sentences...")
for sid in tqdm(sid_lst):
idx_s = df_itemized["study_id"] == sid
df_sid = df_itemized[idx_s]
# unique sentence index
sen_ids = list(df_sid["sentence_ix"].unique())
for si in sen_ids:
idx_sen = df_sid["sentence_ix"] == si
df_sen = df_sid[idx_sen]
sen = df_sen["sentence"].iloc[0]
# step 1, select all target anatomy entities (e.g., lobe) with label = ANAT-DP and target = NaN
idx_a = (df_sen["label"] == "ANAT-DP") & (df_sen["target"].isnull())
df_a = df_sen[idx_a]
if sum(idx_a) > 0:
for _, row_a in df_a.iterrows():
anatomy_source_keys = []
sen = row_a.sentence
source_key = row_a.source
# step 2, get detailed target anatomy (e.g., lower left lobe)
token_a = [row_a["token"].lower()]
anatomy_source_keys.append(source_key)
idx_t = (df_sen["label"] == "ANAT-DP") & (
df_sen["target"] == source_key
)
if sum(idx_t) > 0:
df_t = df_sen[idx_t]
for _, row in df_t.iterrows():
token_a += [row["token"].lower()]
anatomy_source_keys.append(
row["source"]
) # save keys of all anatomy token, i.e., lower, left, lobe
anatomy = "|".join(token_a)
else:
anatomy = row_a["token"].lower()
# step 3: get observations associated with the target anatomy (e.g., normal, effusion)
idx_o = (
(df_sen["label"].isin(["OBS-DA", "OBS-DP", "OBS-U"]))
& (df_sen["target"].isin(anatomy_source_keys))
& (df_sen["relation"] == "located_at")
)
if sum(idx_o) > 0:
df_o = df_sen[idx_o]
anatomy_lst = []
obs_lst = []
label_lst = []
obs_modify_lst = []
obs_suggestive_lst = []
for _, row_o in df_o.iterrows():
anatomy_lst.append(anatomy)
obs_lst.append(row_o["token"].lower())
label_lst.append(row_o["label"])
# step 4: get obs modification
idx_o_m = (df_sen["target"] == row_o.source) & (
df_sen["relation"] == "modify"
)
obs_modify = None
if sum(idx_o_m) > 0:
df_o_m = df_sen[idx_o_m]
temp_lst = []
for _, row_om in df_o_m.iterrows():
# if the modification is present
if row_om.label == "OBS-DP":
temp_lst.append(row_om["token"].lower())
if len(temp_lst) > 0:
obs_modify = "|".join(temp_lst)
obs_modify_lst.append(obs_modify)
# step 5: get suggestive of obs
idx_o_s = (df_sen["target"] == row_o.source) & (
df_sen["relation"] == "suggestive_of"
)
obs_suggestive = None
if sum(idx_o_s) > 0:
df_o_s = df_sen[idx_o_s]
temp_lst = []
for _, row_os in df_o_s.iterrows():
# if the modification is present
if row_os.label == "OBS-DP":
temp_lst.append(row_os["token"].lower())
if len(temp_lst) > 0:
obs_suggestive = "|".join(temp_lst)
obs_suggestive_lst.append(obs_suggestive)
else:
anatomy_lst = [anatomy]
obs_lst = [None]
label_lst = [None]
obs_modify_lst = [None]
obs_suggestive_lst = [None]
# step 4: get observations that are not associated with the target anatomy
idx_oo = (
(df_sen["label"].isin(["OBS-DA", "OBS-DP", "OBS-U"]))
& (df_sen["target"].isna())
& (df_sen["relation"].isna())
)
if sum(idx_oo) > 0:
df_oo = df_sen[idx_oo]
for _, row_oo in df_oo.iterrows():
anatomy_lst.append("unspecified")
obs_lst.append(row_oo["token"].lower())
label_lst.append(row_oo["label"])
# obs_modify_lst.append(None)
# obs_suggestive_lst.append(None)
# step 5: get obs modification
idx_o_m = (df_sen["target"] == row_oo.source) & (
df_sen["relation"] == "modify"
)
obs_modify = None
if sum(idx_o_m) > 0:
df_o_m = df_sen[idx_o_m]
temp_lst = []
for _, row_om in df_o_m.iterrows():
# if the modification is present
if row_om.label == "OBS-DP":
temp_lst.append(row_om["token"].lower())
if len(temp_lst) > 0:
obs_modify = "|".join(temp_lst)
obs_modify_lst.append(obs_modify)
# step 5: get suggestive of obs
idx_o_s = (df_sen["target"] == row_oo.source) & (
df_sen["relation"] == "suggestive_of"
)
obs_suggestive = None
if sum(idx_o_s) > 0:
df_o_s = df_sen[idx_o_s]
temp_lst = []
for _, row_os in df_o_s.iterrows():
# if the modification is present
if row_os.label == "OBS-DP":
temp_lst.append(row_os["token"].lower())
if len(temp_lst) > 0:
obs_suggestive = "|".join(temp_lst)
obs_suggestive_lst.append(obs_suggestive)
# step 6: create tuple of 7 values (sid, sentence_id, sentence, anatomy, obs, label)
t_lst = []
for i in range(len(obs_lst)):
t_lst.append(
(
sid,
si,
sen,
anatomy_lst[i],
obs_lst[i],
label_lst[i],
obs_modify_lst[i],
obs_suggestive_lst[i],
)
)
# remove duplicates caused by 1 obs "located_at" multiple anatomies
tuple_lst.append(list(set(t_lst)))
# if the sentence does not have any ANATOMY token
else:
idx_o = (df_sen["label"].isin(["OBS-DA", "OBS-DP", "OBS-U"])) & (
df_sen["target"].isnull()
)
if sum(idx_o) > 0:
df_o = df_sen[idx_o]
obs_lst = []
label_lst = []
obs_modify_lst = []
obs_suggestive_lst = []
for _, row_o in df_o.iterrows():
obs_lst.append(row_o["token"].lower())
label_lst.append(row_o["label"])
# step 4: get obs modification
idx_o_m = (df_sen["target"] == row_o.source) & (
df_sen["relation"] == "modify"
)
obs_modify = None
if sum(idx_o_m) > 0:
df_o_m = df_sen[idx_o_m]
temp_lst = []
for _, row_om in df_o_m.iterrows():
# if the modification is present
if row_om.label == "OBS-DP":
temp_lst.append(row_om["token"].lower())
if len(temp_lst) > 0:
obs_modify = "|".join(temp_lst)
obs_modify_lst.append(obs_modify)
# step 5: get suggestive of obs
idx_o_s = (df_sen["target"] == row_o.source) & (
df_sen["relation"] == "suggestive_of"
)
obs_suggestive = None
if sum(idx_o_s) > 0:
df_o_s = df_sen[idx_o_s]
temp_lst = []
for _, row_os in df_o_s.iterrows():
# if the modification is present
if row_os.label == "OBS-DP":
temp_lst.append(row_os["token"].lower())
if len(temp_lst) > 0:
obs_suggestive = "|".join(temp_lst)
obs_suggestive_lst.append(obs_suggestive)
else:
obs_lst = [None]
label_lst = [None]
obs_modify_lst = [None]
obs_suggestive_lst = [None]
# step 6: create tuple of 7 values (sid, sentence_id, sentence, anatomy, obs, label)
t_lst = []
for i in range(len(obs_lst)):
t_lst.append(
(
sid,
si,
sen,
"unspecified",
obs_lst[i],
label_lst[i],
obs_modify_lst[i],
obs_suggestive_lst[i],
)
)
# remove duplicates if existing
tuple_lst.append(list(set(t_lst)))
# flatten nested list
df_lst = [item for sublist in tuple_lst for item in sublist]
df_anatomy_label = pd.DataFrame(
df_lst,
columns=[
"study_id",
"sen_id",
"sentence",
"anatomy",
"observation",
"label",
"obs_modify",
"obs_suggestive",
],
)
# lemmatize observation tokens (e.g., normalize opacities to opacity)
obs_lemma_lst = []
print("Lemmatizing observation tokens...")
for t in tqdm(df_lst):
obs = t[4]
obs_lemma = obs_lemmatization(obs)
obs_lemma_lst.append(obs_lemma)
# save preprocessed sentence level data
df_anatomy_label["obs_lemma"] = obs_lemma_lst
df_anatomy_label.to_csv(args.output_path, index=False)
print("Output file has been saved!")
if __name__ == "__main__":
args = parser.parse_args()
radgraph_parse(args)
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