import time
import math
import os
import sys
import os, os.path, shutil
import codecs
import EMRdef
import re
import pandas as pd
emrtxts = EMRdef.txttq(u'D:\DeepLearning ER\EHRzhzd2') #txt目录提取
dis = open(r'C:\Users\Administrator\Desktop\ICD-10.txt', errors='ignore')
ds = dis.readlines()
ds_cs = []
ryzd = []
for line in ds:
line = re.sub('\n', '', line)
ds_cs.append(line)
for emrtxt in emrtxts:
out = []
f = open(emrtxt, 'r', errors="ignore") #中文加入errors
emrpath = os.path.basename(emrtxt)
emrpath = os.path.splitext(emrpath)[0] #提取目录
lines = f.readlines()
for line in lines:
line = re.sub('\n', '', line)
while ds_c in ds_cs:
if set(line) == set(ds_c):
out.append(ds_c)
elif line.find(ds_c) > -1:
out.append(ds_c)
#-*- coding: UTF-8 -*-
#本文件用于数据清洗
import time
import math
import os
import sys
import os, os.path, shutil
import codecs
import EMRdef
import re
emrtxts = EMRdef.txttq(u'D:\DeepLearning ER\EHRryzd') #txt目录提取
hxjb = open(r'D:\python\EMR\hxjbml.txt', errors="ignore") #呼吸疾病目录
hxjbdic = hxjb.readlines() #读行
ryzd = []
for emrtxt in emrtxts:
f = open(emrtxt, 'r', errors="ignore") #中文加入errors
emrpath = os.path.basename(emrtxt)
emrpath = os.path.splitext(emrpath)[0]
line_out = []
for line in f.readlines():
line = re.sub('\n', '', line)
line = re.sub(r'(.+?)肺炎', '肺炎', line) #替换所有的肺炎
for hxjbc in hxjbdic: #检索每个词
hxjbc = re.sub('\n', '', hxjbc)
if line.find(hxjbc) > -1:
line_out.append(line)
line_output = EMRdef.delre(line_out)
ryzd.append(line_out)
#line = '\n'.join(line_output)
#EMRdef.text_create(r'D:\DeepLearning ER\EHRryzd2','.txt' ,emrpath,line)
import time
import math
import os
import sys
import os, os.path, shutil
import codecs
import EMRdef
import re
import pandas as pd
emrtxts = EMRdef.txttq(u'D:\DeepLearning ER\EHRzhzd5') #txt目录提取
emrtxt2s = EMRdef.txttq(u'D:\DeepLearning ER\EHRsex')
ryzd = []
for emrtxt in emrtxts:
f = open(emrtxt, 'r', errors="ignore") #中文加入errors
emrpath = os.path.basename(emrtxt)
emrpath = os.path.splitext(emrpath)[0] #提取目录
lines = f.readlines()
lines = ''.join(lines)
lines = re.sub(' ', '', lines)
lines = re.split('\n', lines)
for emrtxt2 in emrtxt2s:
f2 = open(emrtxt2, 'r', errors="ignore") #中文加入errors
emrpath2 = os.path.basename(emrtxt2)
emrpath2 = os.path.splitext(emrpath2)[0] #提取目录
lines2 = f2.readlines()
lines2 = ''.join(lines2)
if emrpath == emrpath2:
lines.append(lines2)
ryzd.append(lines)
print
#-*- coding: UTF-8 -*-
#本文件用于提取目标目录中的所有txt，并提取关键词所在行到指定目录，并提取关键词新建文件
import time
import math
import os
import sys
import os, os.path, shutil
import codecs
import EMRdef
import re
emrtxts = EMRdef.txttq(u'C:\Users\Administrator\Desktop\xiaochuan') #txt目录提取
for emrtxt in emrtxts:
f = open(emrtxt, 'r', errors="ignore") #中文加入errors
emrtxt = os.path.basename(emrtxt)
emrtxt_str = re.findall(r'(^.+?)\_', emrtxt) #提取ID
emrtxt = "".join(emrtxt_str) #转成str
pattern = r',|.|，|。|;|；' #清除标点
#txtp=txtp.decode('utf-8')
for line in f.readlines():
line = re.sub(' ', '', line) #删除空格
if line.find(u'入院诊断：', 0, 6) > -1:
line = re.sub(r'h|H', '小时', line) #小时替换成中文
line = re.sub(r'入院诊断：', '', line) #删除入院诊断字样
line_deldl = re.split(r'；|。|，|;|？', line) #根据标点分行
line_deld = '\n'.join(line_deldl) #转成str格式
line_out = re.sub(r'\d+、', '\n', line_deld) #删除序号
line_output = re.split('\n', line_out)
line = '\n'.join(line_output)
EMRdef.text_create(r'F:\zljh', '.txt', emrtxt,
#根据词典提取
#-*- coding: UTF-8 -*-
#本文件用于根据指标参数提取所有指标
import os
import EMRdef
import re
#根据句号分词
emrtxt2s = EMRdef.txttq(u'D:\DeepLearning ER\EHRzlgc') #txt目录提取
pattern2 = r'、|；|：|、|:|，' #根据标点分词
for emrtxt2 in emrtxt2s:
f2 = open(emrtxt2, 'r', errors="ignore") #中文加入errors
f2_end = re.split(pattern2, f2.read())
f2_out = "\n".join(f2_end) #转成str
emrpath2 = os.path.basename(emrtxt2)
emrpath2 = os.path.splitext(emrpath2)[0]
EMRdef.text_create(u'D:\DeepLearning ER\EHRzlgc3', '.txt', emrpath2,
f2_out)
#EMRdef.text_save(emrtxt,f_end)
'''----------------------------------------------------------------------------------------------------------------------------------------------'''
#根据化验指标提取段落
b = open('D:\python\EMR\hyzb.txt', 'r', errors="ignore")
emrtxts = EMRdef.txttq(u'D:\DeepLearning ER\EHRzlgc3') #txt目录提取
pattern = r',|;|\'|`|\[|\]|<|>|\?|"|\{|\}|!|@|#|\$|%|\^|&|=|，|。|：|；|‘|’|【|】|（|）|·|！|、|…' #清除标点
brl = b.readlines()
for emrtxt in emrtxts:
f = open(emrtxt, 'r', errors="ignore") #中文加入errors
emrpath = os.path.basename(emrtxt)
emrpath = os.path.splitext(emrpath)[0] #提取目录
test_out = []
import time
import math
import os
import sys
import os, os.path, shutil
import codecs
import EMRdef
import re
emrtxts = EMRdef.txttq(u'D:\DeepLearning ER\EHRryzd') #txt目录提取
emrtxt2s = EMRdef.txttq(r'D:\DeepLearning ER\EHRzlgc4') #txt目录提取
out = []
for emrtxt in emrtxts:
emrpath = os.path.basename(emrtxt)
emrpath = os.path.splitext(emrpath)[0]
for emrtxt2 in emrtxt2s:
emrpath2 = os.path.basename(emrtxt2)
emrpath2 = os.path.splitext(emrpath2)[0]
if emrpath == emrpath2:
f = open(emrtxt, 'r', errors="ignore") #中文加入errors
f2 = open(emrtxt2, 'r', errors="ignore") #中文加入errors
a = f.readlines()
b = f2.readlines()
c = b + a
#-*- coding: UTF-8 -*-
#根据给药方式和剂量剂型分词
import os
import EMRdef
import string
import re
emrtxts = EMRdef.txttq(r'D:\DeepLearning ER\EHRzlgc4') #txt目录提取
#pattern = r',|;|\*|`|\[|\]|<|>|\?|"|\{|\}|!|@|#|\$|%|\^|&|=|，|。|：|；|‘|’|\+|\-|【|】| \)|\( |（|）|·|！|、|…'#清除标点
pattern = r',|;|\'|`|\[|\]|<|>|\?|"|\{|\}|!|@|#|\$|%|\^|&|=|＞|，|。|：|＜|；|‘|’|【|】|（|）|·|！|\*|\/|…' #清除标点
hyjg = []
for emrtxt in emrtxts:
f = open(emrtxt, 'r', errors="ignore") #中文加入errors
emrpath = os.path.basename(emrtxt)
emrpath = os.path.splitext(emrpath)[0] #提取目录
f_end = []
for line in f.readlines():
c = line
line = re.sub(' ', '', line) #删除空格
line = re.sub('\.', '', line) #删除.
line = re.sub('×', '', line) #删除.
a = EMRdef.tq_bnum(line)
a_end = "".join(a) #转成str
a_end = re.split(pattern, a_end)
a_end = "".join(a_end) #转成str
a_end = re.sub(' ', '', a_end) #删除空格
a_end = "".join(a_end) #转成str
if a_end == '':
a_end = 1
else:
acb = EMRdef.rre(c, a_end, a_end + ':', 1)
