if '2、' in line:
str12 = str(line.split('、'))
out.append(str12)
if '3、' in line:
str13 = str(line.split('、'))
out.append(str13)
if '4、' in line:
str14 = str(line.split('、'))
out.append(str14)
if '5、' in line:
str15 = str(line.split('、'))
out.append(str15)
if '6、' in line:
str16 = str(line.split('、'))
out.append(str16)
if '7、' in line:
str17 = str(line.split('、'))
out.append(str17)
if '8、' in line:
str18 = str(line.split('、'))
out.append(str18)
if '9、' in line:
str19 = str(line.split('、'))
out.append(str19)
if '10、' in line:
str20 = str(line.split('、'))
out.append(str20)
out=EMRdef.delre(out)
line = ''.join(out)
EMRdef.text_create(r'D:\DeepLearning ER\EHRzhzd2','.txt',emrpath,line)
line4 = str(line3)
line = line4
line = re.sub('\n', '', line)
line = re.sub(' ', '', line)
line = re.sub(r'\?|？', '', line)
line = re.sub(r'\,|\.|；', '', line)
out = line
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)
line = out
line_re.append(line)
while '' in line_re:
line_re.remove('')
output = EMRdef.delre(line_re)
output1 = '\n'.join(output)
EMRdef.text_create(r'D:\DeepLearning ER\EHRzhzd2', '.txt', emrpath,
output1)
ryzd.append(output)
#导入关联规则
import orangecontrib.associate.fpgrowth as oaf
def dealRules(rules):
returnRules = []
for i in rules:
temStr = ''
for j in i[0]: #处理第一个frozenset
temStr = temStr + j + '&'
# 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
a_out.append(a_end)
adult_a = EMRdef.delre(a_out)
EMRdef.text_save('D:\python\EMR\hyxm.txt', adult_a)
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 orangecontrib.associate.fpgrowth as oaf
often = dict(oaf.frequent_itemsets(ryzd, .01)) #生成频繁度
rules = oaf.association_rules(often, .01, frozenset({'肺炎'})) #这里设置置信度
rules = list(rules)
print(rules)
#-*- 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)
