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')
if line.find(r'女') > -1:
out.append('W')
if line.find('岁') > -1:
line = re.sub('岁', '', line)
line = ''.join(line)
se = int(line)
if se <= 20:
a = 'Yo'
out.append(line)
break
output = ' '.join(out)
EMRdef.text_create(r'D:\DeepLearning ER\EHRbase', '.txt', emrtxt, output)
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)
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, ds_c) > 0.6 and EMRdef.SBS(line, ds_c) < 1:
out.append(ds_c)
out = EMRdef.delre(out)
output = '\n'.join(out)
EMRdef.text_create(r'D:\DeepLearning ER\EHRzhzd3', '.txt', emrpath, output)
#-*- 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('吸入')>-1:or line.find('')>-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 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)#小时替换成中文
line = re.sub(r'：', '', 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+、','',line_deld) #删除序号
line_output = re.split('\n',line_out)
line = '\n'.join(line_output)
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)
#EMRdef.text_save('D:\python\EMR\zljh.txt',zljhs)
#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))
line = '\n'.join(temp)
EMRdef.text_create(r'D:\DeepLearning ER\EHRzhzd', '.txt', emrtxt, line)
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)
out = line4
out = re.sub(r'右侧|两侧|双侧|左侧|右|左|双', '', out)
out = re.sub(r'肺肺', '肺', out)
out = re.sub('（.*?）', '', out)
out = re.sub(r'很高危|极高危', '', out)
out = re.sub(r'\?|？', '', out)
out = re.sub(r',|.|；|', '')
for ds in ds_cs:
if EMRdef.SBS(out, ds) > 0.8:
output.append(out)
output = EMRdef.delre(output)
output1 = '\n'.join(output)
EMRdef.text_create(r'D:\DeepLearning ER', '.txt', emrpath, output1)
#-*- 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]
for line in f.readlines():
if line.find('吸入')>-1:
f_out =
EMRdef.text_create(u'D:\DeepLearning ER\EHR2','.txt',emrpath,f_out)
#EMRdef.text_save(emrtxt,f_end)
#-*- 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 = []
adult = []
adult_c = []
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():
line = re.sub(' ', '', line)
line = re.sub('\n', '', line)
line = ''.join(line)
if line == '男' or line == '男性':
out.append('M')
elif line == '女' or line == '女性':
out.append('W')
output = ' '.join(out)
EMRdef.text_create(r'D:\DeepLearning ER\EHRsex', '.txt', emrtxt, output)
out = []
for line in f.readlines():
if line.find('岁') > -1:
line = re.sub('岁', '', line)
line = ''.join(line)
out.append(line)
break
'''
se = int(line)
if se <=20:
a = 'Child'
elif se <=40:
a = 'Younth'
elif se <= 60:
a = 'Mid'
#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)'''
'''------------------------------------------------------------------------------------------------------------'''
#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'体格检查') >-1:
line = re.sub('体格检查：','',line)
f2_end = re.split(pattern2,line)
f2_out = "\n".join(f2_end)#转成str
EMRdef.text_create(r'D:\DeepLearning ER\EHRtigejiancha','.txt' ,emrtxt,f2_out)#导出
#zljhs.append(emrtxt+':'+line)
#EMRdef.text_save('D:\python\EMR\zljh.txt',zljhs)'''
'''------------------------------------------------------------------------------------------------------------'''
#-*- 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
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'') >-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)'''
'''------------------------------------------------------------------------------------------------------------'''
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)
f_end.append(acb)
f_out = "".join(f_end) #转成str
EMRdef.text_create(r'D:\DeepLearning ER\EHRzlgc5', '.txt', emrpath, f_out)
