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preprocess_mimic3.py
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import pandas as pd
from collections import Counter, defaultdict
import csv
import operator
from utils.options import args
from utils.utils import build_vocab, word_embeddings, fasttext_embeddings, gensim_to_fasttext_embeddings, gensim_to_embeddings, \
reformat, write_discharge_summaries, concat_data, split_data
Y = 'full'
notes_file = '%s/NOTEEVENTS.csv' % args.MIMIC_3_DIR
# step 1: process code-related files
dfproc = pd.read_csv('%s/PROCEDURES_ICD.csv' % args.MIMIC_3_DIR)
dfdiag = pd.read_csv('%s/DIAGNOSES_ICD.csv' % args.MIMIC_3_DIR)
dfdiag['absolute_code'] = dfdiag.apply(lambda row: str(reformat(str(row[4]), True)), axis=1)
dfproc['absolute_code'] = dfproc.apply(lambda row: str(reformat(str(row[4]), False)), axis=1)
dfcodes = pd.concat([dfdiag, dfproc])
dfcodes.to_csv('%s/ALL_CODES.csv' % args.MIMIC_3_DIR, index=False,
columns=['ROW_ID', 'SUBJECT_ID', 'HADM_ID', 'SEQ_NUM', 'absolute_code'],
header=['ROW_ID', 'SUBJECT_ID', 'HADM_ID', 'SEQ_NUM', 'ICD9_CODE'])
df = pd.read_csv('%s/ALL_CODES.csv' % args.MIMIC_3_DIR, dtype={"ICD9_CODE": str})
print("unique ICD9 code: {}".format(len(df['ICD9_CODE'].unique())))
# step 2: process notes
min_sentence_len = 3
disch_full_file = write_discharge_summaries("%s/disch_full.csv" % args.MIMIC_3_DIR, min_sentence_len, '%s/NOTEEVENTS.csv' % (args.MIMIC_3_DIR))
df = pd.read_csv('%s/disch_full.csv' % args.MIMIC_3_DIR)
df = df.sort_values(['SUBJECT_ID', 'HADM_ID'])
# step 3: filter out the codes that not emerge in notes
hadm_ids = set(df['HADM_ID'])
with open('%s/ALL_CODES.csv' % args.MIMIC_3_DIR, 'r') as lf:
with open('%s/ALL_CODES_filtered.csv' % args.MIMIC_3_DIR, 'w', newline='') as of:
w = csv.writer(of)
w.writerow(['SUBJECT_ID', 'HADM_ID', 'ICD9_CODE', 'ADMITTIME', 'DISCHTIME'])
r = csv.reader(lf)
#header
next(r)
for i,row in enumerate(r):
hadm_id = int(row[2])
#print(hadm_id)
#break
if hadm_id in hadm_ids:
w.writerow(row[1:3] + [row[-1], '', ''])
dfl = pd.read_csv('%s/ALL_CODES_filtered.csv' % args.MIMIC_3_DIR, index_col=None)
dfl = dfl.sort_values(['SUBJECT_ID', 'HADM_ID'])
dfl.to_csv('%s/ALL_CODES_filtered.csv' % args.MIMIC_3_DIR, index=False)
sorted_file = '%s/disch_full.csv' % args.MIMIC_3_DIR
df.to_csv(sorted_file, index=False)
# step 4: link notes with their code
labeled = concat_data('%s/ALL_CODES_filtered.csv' % args.MIMIC_3_DIR, sorted_file, '%s/notes_labeled.csv' % args.MIMIC_3_DIR)
dfnl = pd.read_csv(labeled)
# step 5: statistic unique word, total word, HADM_ID number
types = set()
num_tok = 0
for row in dfnl.itertuples():
for w in row[3].split():
types.add(w)
num_tok += 1
print("num types", len(types), "num tokens", num_tok)
print("HADM_ID: {}".format(len(dfnl['HADM_ID'].unique())))
print("SUBJECT_ID: {}".format(len(dfnl['SUBJECT_ID'].unique())))
# step 6: split data into train dev test
fname = '%s/notes_labeled.csv' % args.MIMIC_3_DIR
base_name = "%s/disch" % args.MIMIC_3_DIR #for output
tr, dv, te = split_data(fname, base_name, args.MIMIC_3_DIR)
vocab_min = 3
vname = '%s/vocab.csv' % args.MIMIC_3_DIR
build_vocab(vocab_min, tr, vname, True)
# build vocab for RAC model
vocab_min = 3
vname = '%s/vocab_rac.csv' % args.MIMIC_3_DIR
build_vocab(vocab_min, tr, vname, True)
# step 7: sort data by its note length, add length to the last column
for splt in ['train', 'dev', 'test']:
filename = '%s/disch_%s_split.csv' % (args.MIMIC_3_DIR, splt)
df = pd.read_csv(filename)
df['length'] = df.apply(lambda row: len(str(row['TEXT']).split()), axis=1)
df = df.sort_values(['length'])
df.to_csv('%s/%s_full.csv' % (args.MIMIC_3_DIR, splt), index=False)
# step 8: train word embeddings via word2vec and fasttext
w2v_file = word_embeddings('full', '%s/disch_full.csv' % args.MIMIC_3_DIR, 100, 0, 5)
gensim_to_embeddings('%s/processed_full_100.w2v' % args.MIMIC_3_DIR, '%s/vocab.csv' % args.MIMIC_3_DIR, Y)
# fasttext_file = fasttext_embeddings('full', '%s/disch_full.csv' % args.MIMIC_3_DIR, 100, 0, 5)
# gensim_to_fasttext_embeddings('%s/processed_full_100.fasttext' % args.MIMIC_3_DIR, '%s/vocab.csv' % args.MIMIC_3_DIR, Y)
# generate word embeddings (300 dimensions) for convolved embedding model
w2v_file = word_embeddings('full', '%s/disch_full.csv' % args.MIMIC_3_DIR, 300, 0, 5)
gensim_to_embeddings('%s/processed_full_300.w2v' % args.MIMIC_3_DIR, '%s/vocab_rac.csv' % args.MIMIC_3_DIR, Y)
# fasttext_file = fasttext_embeddings('full', '%s/disch_full.csv' % args.MIMIC_3_DIR, 300, 10, 5)
# gensim_to_fasttext_embeddings('%s/processed_full_300.fasttext' % args.MIMIC_3_DIR, '%s/vocab_rac.csv' % args.MIMIC_3_DIR, Y)
# step 9: statistic the top 50 code
Y = 50
counts = Counter()
dfnl = pd.read_csv('%s/notes_labeled.csv' % args.MIMIC_3_DIR)
for row in dfnl.itertuples():
for label in str(row[4]).split(';'):
counts[label] += 1
codes_50 = sorted(counts.items(), key=operator.itemgetter(1), reverse=True)
codes_50 = [code[0] for code in codes_50[:Y]]
with open('%s/TOP_%s_CODES.csv' % (args.MIMIC_3_DIR, str(Y)), 'w', newline='') as of:
w = csv.writer(of)
for code in codes_50:
w.writerow([code])
# step 10: split data according to train_50_hadm_ids dev... and test...
for splt in ['train', 'dev', 'test']:
print(splt)
hadm_ids = set()
with open('%s/%s_50_hadm_ids.csv' % (args.MIMIC_3_DIR, splt), 'r') as f:
for line in f:
hadm_ids.add(line.rstrip())
with open('%s/notes_labeled.csv' % args.MIMIC_3_DIR, 'r') as f:
with open('%s/%s_%s.csv' % (args.MIMIC_3_DIR, splt, str(Y)), 'w', newline='') as of:
r = csv.reader(f)
w = csv.writer(of)
#header
w.writerow(next(r))
i = 0
for row in r:
hadm_id = row[1]
if hadm_id not in hadm_ids:
continue
codes = set(str(row[3]).split(';'))
filtered_codes = codes.intersection(set(codes_50))
if len(filtered_codes) > 0:
w.writerow(row[:3] + [';'.join(filtered_codes)])
i += 1
# step 11: sort data by its note length, add length to the last column
for splt in ['train', 'dev', 'test']:
filename = '%s/%s_%s.csv' % (args.MIMIC_3_DIR, splt, str(Y))
df = pd.read_csv(filename)
df['length'] = df.apply(lambda row: len(str(row['TEXT']).split()), axis=1)
df = df.sort_values(['length'])
df.to_csv('%s/%s_%s.csv' % (args.MIMIC_3_DIR, splt, str(Y)), index=False)