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\EHRzhzd4') #txt目录提取
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()
if len(lines) > 1: #对疾病数量判断
lines = ''.join(lines)
output = re.sub('\n', ' ', lines)
out
EMRdef.text_create(r'D:\DeepLearning ER\EHRzhzd6', '.txt', emrpath,
output)
'''
out = re.split('',output)
ryzd.append(out)
'''
#导入关联规则
import orangecontrib.associate.fpgrowth as oaf
def dealRules(rules):
#-*- 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'D:\DeepLearning ER\EHR') #txt目录提取
zljhs = []
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)
line = re.sub(r'入院诊断：', '', line)
line_out = re.split()
EMRdef.text_create(r'D:\DeepLearning ER\EHRryzd', '.txt', emrtxt,
line) #导出带有诊疗计划的文件和诊疗计划
#zljhs.append(emrtxt+':'+line)
#-*- 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
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)
#f_end = re.split(pattern, f_start2)
#-*- coding: UTF-8 -*-
import re
import EMRdef
import os, os.path,shutil
emrtxts = EMRdef.txttq(u'D:\DeepLearning ER\EHR1')#txt目录提取
pattern = r',|;|\'|`|\[|\]|<|>|\?|"|\{|\}|!|@|#|\$|\^|&|=|，|。|：|；|‘|’|【|】|·|！|、|…'#根据标点分词
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]
f_out = f.readlines()
for line in f_out:
if line.find('都保')>-1 or line.find('舒利迭')>-1:
f_out = ''.join(f_out)
EMRdef.text_create(u'D:\DeepLearning ER\EHRxiaochuan','.txt',emrpath,f_out)
#EMRdef.text_save(emrtxt,f_end)
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\EHR-all')#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
out = []
for line in f.readlines():
if line.find('男')>-1:
out.append('男')
elif line.find('女')>-1:
out.append('女')
if line.find(‘岁')
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()
line = ''.join(lines)
lines = re.sub(' ','',line)
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)
out = '\n'.join(lines)
EMRdef.text_create(r'D:\DeepLearning ER\EHRzhzd6','.txt',emrpath,out)
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', output)
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)
#-*- 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，关键词 诊疗过程
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\EHRzlgc4') #txt目录提取
a_out = []
pattern = r',|;|\'|`|\[|\]|<|>|\?|"|\{|\}|!|@|#|\$|%|\^|&|=|＞|，|。|：|＜|；|‘|’|【|】|（|）|·|！|\*|\/|…' #清除标点
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
#txtp=txtp.decode('utf-8')
for line in f.readlines():
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
#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 = []
C.append(i)
return C
import re
f = open('D:\DeepLearning ER\Z1006014.txt', 'r', errors='ignore')
g = open(r'C:\Users\Administrator\Desktop\ICD-10.txt', 'r', errors='ignore')
line_re = []
lines = f.readlines()
dics = g.readlines()
out = []
for line in lines:
line = re.sub('\n', '', line)
line = re.sub(' ', '', line)
line = re.sub(r'\?|？', '', line)
line = re.sub(r'\,|\.|；', '', line)
line_re.append(line)
while '' in line_re:
line_re.remove('')
for line in line_re:
for dic in dics:
dic = re.sub('\n', '', dic)
if set(line) == set(dic):
out.append(dic)
elif SBS(line, dic) > 0.8 and SBS(line, dic) < 1:
out.append(dic)
import EMRdef
out = EMRdef.delre(out)
EMRdef.dic_save(r'D:\DeepLearning ER\JBML.txt', 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 -*-
#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，关键词 主诉
import os
import sys
import os, os.path,shutil
import codecs
import EMRdef
import re
#关键词提取 关键词为诊疗计划
emrtxts = EMRdef.txttq(u'D:\DeepLearning ER\EHR')#txt目录提取
pattern2 = r'。|：|、|，'#根据标点分词
zljhs = []
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
#txtp=txtp.decode('utf-8')
for line in f.readlines():
line = re.sub(' ','',line)#删除空格
if line.find (u'诊疗计划') >-1:
#line = re.sub('主诉:','',line)
f2_end = re.split(pattern2,line)
f2_out = "\n".join(f2_end)#转成str
EMRdef.text_create(r'D:\DeepLearning ER\EHRzhusu','.txt' ,emrtxt,f2_out)#导出
#zljhs.append(emrtxt+':'+line)
#EMRdef.text_save('D:\python\EMR\zljh.txt',zljhs)'''
'''------------------------------------------------------------------------------------------------------------'''
#-*- 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)
#-*- coding: UTF-8 -*-
#本文件用于提取给药方式
import os
import EMRdef
import re
pattern = r',|;|\'|`|\[|\]|<|>|\?|"|\{|\}|!|@|#|\$|\^|&|=|，|。|：|；|‘|’|【|】|·|！|、|…'#根据标点分词
b = open('D:\python\EMR\967ywml.txt','r',errors="ignore")
brl = b.readlines()
adult = []
adult_c = []
for bl in brl:
bl = re.sub('\n','',bl)
bl = re.sub('','',bl)
adult.append(bl)
adult_c = EMRdef.delre(adult)
EMRdef.text_save(u'D:\python\EMR\967yw.txt',adult_c)
#-*- coding: UTF-8 -*-
#
#提取最后诊断之后的内容 并进入下一步处理
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\EHR-all') #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')
temp = f.readlines()
tem_del = []
for line in temp:
tem_del.append(line)
if line.find(u'初步诊断') > -1:
break
elif line.find(u'最后诊断') > -1:
break
elif line.find(u'最后诊断') > -1:
break
temp = list(set(temp) - set(tem_del))
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\EHRzhzd')#txt目录提取
dis = open(r'C:\Users\Administrator\Desktop\ICD-10.txt',errors='ignore')
ds=dis.readlines()
ds_cs = []
for line in ds:
line = re.sub('\n','',line)
ds_cs.append(line)
ryzd=[]
output=[]
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
pattern =r'\s*\d+、+\s?(.*)'
c=re.compile(pattern)
for line in f.readlines():
line1=line.strip('\n')
line2 = ''.join(line1)
line2 = line2.strip( )
line3=c.findall(line2)
line3=''.join(line3)
line4 = str(line3)
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\EHRzhzd') #txt目录提取
g = open(r'C:\Users\Administrator\Desktop\JBML.txt', errors='ignore')
dics = g.readlines()
ryzd = []
output = []
line_re = []
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
pattern = r'\s*\d+、+\s?(.*)'
c = re.compile(pattern)
for line in f.readlines():
line1 = line.strip('\n')
line2 = ''.join(line1)
line2 = line2.strip()
line3 = c.findall(line2)
line3 = ''.join(line3)
line4 = str(line3)
line = line4
line = re.sub('\n', '', 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\EHRzhzd')#txt目录提取
dis = open(r'C:\Users\Administrator\Desktop\ICD-10.txt',errors='ignore')
ds=dis.readlines()
ds_cs = []
for line in ds:
line = re.sub('\n','',line)
ds_cs.append(line)
ryzd=[]
output=[]
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
pattern =r'\s*\d+、+\s?(.*)'
c=re.compile(pattern)
for line in f.readlines():
line1=line.strip('\n')
line2 = ''.join(line1)
line2 = line2.strip( )
line3=c.findall(line2)
line3=''.join(line3)
line4 = str(line3)
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\EHRzhzd') #txt目录提取
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
pattern = r'\s*\d+、+\s?(.*)'
c = re.compile(pattern)
output = []
for line in f.readlines():
line1 = line.strip('\n')
line2 = ''.join(line1)
line2 = line2.strip()
line3 = c.findall(line2)
line3 = ''.join(line3)
line4 = str(line3)
out = line4
if out.find('肺')>-1 or out.find('呼吸')>-1 or out.find('气管')>-1 or out.find('呼吸')>-1 \
or out.find('筛窦')>-1 or out.find('上额窦')>-1 or out.find('胸腔')>-1 or out.find('鼻')>-1 \
or out.find('蝶窦')>-1 or out.find('蝶窦')>-1 :
output.append(out)
output = EMRdef.delre(output)
output1 = '\n'.join(output)
EMRdef.text_create(r'D:\DeepLearning ER\EHRzhzd2', '.txt', emrpath,
C.append(i)
return C
import re
f = open('D:\DeepLearning ER\Z1006014.txt', 'r', errors='ignore')
g = open(r'C:\Users\Administrator\Desktop\ICD-10.txt', 'r', errors='ignore')
line_re = []
lines = f.readlines()
dics = g.readlines()
out = []
for line in lines:
line = re.sub('\n', '', line)
line = re.sub(' ', '', line)
line = re.sub(r'\?|？', '', line)
line = re.sub(r'\,|\.|；', '', line)
line_re.append(line)
while '' in line_re:
line_re.remove('')
for line in line_re:
for dic in dics:
dic = re.sub('\n', '', dic)
if set(line) == set(dic):
out.append(dic)
elif SBS(line, dic) > 0.9:
out.append(dic)
import EMRdef
out = EMRdef.delre(out)
print(out)
# -*- coding:utf-8 -*-
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\EHR-all')#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
out = []
for line in f.readlines():
if line.find(r'男')>-1:
out.append('M')
elif line.find(r'女')>-1:
out.append('W')
if line.find('岁')>-1:
line = re.sub('岁','',line)
line = ''.join(line)
output = ' '.join(out)
EMRdef.text_create(r'D:\DeepLearning ER\EHRbase','.txt' ,emrtxt,output)
se = int(line)
if se <=20:
a = 'Child'
elif se <=40:
#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
import pandas as pd
import numpy as np
#设置pandas参数
np.set_printoptions(threshold=np.inf)
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
line_out = []
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]
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)
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\EHRzhzd') #txt目录提取
dis = open(r'C:\Users\Administrator\Desktop\ICD-10.txt', errors='ignore')
ds = dis.readlines()
ds_cs = []
for line in ds:
line = re.sub('\n', '', line)
ds_cs.append(line)
ryzd = []
output = []
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
pattern = r'\s*\d+、+\s?(.*)'
c = re.compile(pattern)
for line in f.readlines():
line1 = line.strip('\n')
line2 = ''.join(line1)
line2 = line2.strip()
line3 = c.findall(line2)
line3 = ''.join(line3)
line4 = str(line3)
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\EHRzhzd')#txt目录提取
g = open(r'C:\Users\Administrator\Desktop\JBML.txt',errors='ignore')
dics=g.readlines()
ryzd=[]
output=[]
line_re = []
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
pattern =r'\s*\d+、+\s?(.*)'
c=re.compile(pattern)
for line in f.readlines():
line1=line.strip('\n')
line2 = ''.join(line1)
line2 = line2.strip( )
line3=c.findall(line2)
line3=''.join(line3)
line4 = str(line3)
line = line4
line=re.sub('\n','',line)
line=re.sub(' ','',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\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)
for ds_c in ds_cs:
if line.find(ds_c) > -1:
out.append(d)
if set(line) == set(ds_c):
out.append(ds_c)
elif EMRdef.SBS(line, ds_c) > 0.6 and EMRdef.SBS(line, ds_c) < 1:
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\JBML.txt',errors='ignore')
ds=dis.readlines()
ds_cs = []
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)
for ds_c in ds_cs:
if set(line) == set(ds_c):
out.append(ds_c)
elif EMRdef.SBS(line,dic)>0.8 and SBS(line,dic) <1:
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\EHRzhzd4') #txt目录提取
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
#-*- 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，关键词 主诉
import os
import sys
import os, os.path,shutil
import codecs
import EMRdef
import re
#关键词提取 关键词为诊疗计划
emrtxts = EMRdef.txttq(u'D:\DeepLearning ER\EHR1')#txt目录提取
pattern2 = r'。|：|、|，'#根据标点分词
zljhs = []
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
a_out = f.readlines()
#txtp=txtp.decode('utf-8')
for line in a_out:
line = re.sub(' ','',line)#删除空格
if line.find (u'吸入剂') >-1:
f2_out = "".join(a_out)#转成str
EMRdef.text_create(r'D:\DeepLearning ER\EHRxiaochuan','.txt' ,emrtxt,f2_out)#导出
#zljhs.append(emrtxt+':'+line)
#EMRdef.text_save('D:\python\EMR\zljh.txt',zljhs)'''
'''------------------------------------------------------------------------------------------------------------'''
