[人工智能]Python 电子病历（EMR）机器学习和深度学习数据预处理，医学文本标签数据预处理

数据清洗过程，将原始数据处理成可用于机器学习或者深度学习训练的数据。

数据原始格式：

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?数据和代码，参考了刘焕勇老师的数据和分享

1.导入需要的包（jupyter notebook）

import os
from collections import Counter

2.事先定义好数据标签：（jupyter notebook）

label_dict = {
      '检查和检验': 'CHECK',
      '症状和体征': 'SIGNS',
      '疾病和诊断': 'DISEASE',
      '治疗': 'TREATMENT',
      '身体部位': 'BODY'}

cate_dict ={
         'O':0,
         'TREATMENT-I': 1,
         'TREATMENT-B': 2,
         'BODY-B': 3,
         'BODY-I': 4,
         'SIGNS-I': 5,
         'SIGNS-B': 6,
         'CHECK-B': 7,
         'CHECK-I': 8,
         'DISEASE-I': 9,
         'DISEASE-B': 10
        }

3.定位到文件夹路径，此文件夹为data（父文件夹），还有四个子文件夹，定位到data_transfer_process.ipynb这个python执行文件的绝对路径的上一层：（jupyter notebook）

cur = '\\'.join(os.path.abspath('data_transfer_process.ipynb').split('\\')[:-1])

?注意在windows系统中，路径是这样的，但是在服务器上，是这样的

cur_01 = '/'.join(os.path.abspath('data_transfer_process.ipynb').split('/')[:-1])

要不然，split是切分不开的！

4.创建变量： 原始数据路径，输出数据路径（jupyter notebook）

#原始数据路径
origin_path = os.path.join(cur, 'data_origin')
#输出数据路径
train_filepath = os.path.join(cur, 'train.txt')

5.数据处理过程：（jupyter notebook）

f = open(train_filepath, 'w+',encoding='utf-8')
for root,dirs,files in os.walk(origin_path):
#     print('root:',root)
#     print('dirs:',dirs)
#     print('files:',files)
    for file in files:
        filepath = os.path.join(root, file)
        if 'original' not in filepath:
            continue
#         print(type(filepath))
        label_fliepath = filepath.replace('.txtoriginal','')
#         print(filepath,'\n',label_fliepath)
        content = open(filepath,encoding='utf-8').read().strip()
#         print(content)
        res_dict = {}
        for line in open(label_fliepath,encoding='utf-8'):
            res = line.strip().split('\t')
            start = res[1]
            end = res[2]
            label = res[3]
            label_id = label_dict.get(label)
            for i in range(int(start),int(end)+1):
                if i ==int(start):
                    label_cate = label_id + '-B'
                else:
                    label_cate = label_id + '-I'
                res_dict[i] = label_cate
#             print('res:',res)
#             print('res_dict:',res_dict)
#             break
        for indx,char in enumerate(content):
#             print(indx,content)
            char_label = res_dict.get(indx,'0')
            print(indx,'\t',char,'\t',char_label)
            f.write(char + '\t' + char_label + '\n')
#             break
#        break
f.close()

6.完整代码（pycharm）：

import os
from collections import Counter

class TransferData:
    def __init__(self):
        cur = '/'.join(os.path.abspath(__file__).split('/')[:-1])
        self.label_dict = {
                      '检查和检验': 'CHECK',
                      '症状和体征': 'SIGNS',
                      '疾病和诊断': 'DISEASE',
                      '治疗': 'TREATMENT',
                      '身体部位': 'BODY'}

        self.cate_dict ={
                         'O':0,
                         'TREATMENT-I': 1,
                         'TREATMENT-B': 2,
                         'BODY-B': 3,
                         'BODY-I': 4,
                         'SIGNS-I': 5,
                         'SIGNS-B': 6,
                         'CHECK-B': 7,
                         'CHECK-I': 8,
                         'DISEASE-I': 9,
                         'DISEASE-B': 10
                        }
        self.origin_path = os.path.join(cur, 'data_origin')
        self.train_filepath = os.path.join(cur, 'train.txt')
        return


    def transfer(self):
        f = open(self.train_filepath, 'w+',encoding='utf-8')
        count = 0
        for root,dirs,files in os.walk(self.origin_path):
            for file in files:
                filepath = os.path.join(root, file)
                if 'original' not in filepath:
                    continue
                label_filepath = filepath.replace('.txtoriginal','')
                print(filepath, '\t\t', label_filepath)
                content = open(filepath,encoding='utf-8').read().strip()
                print(content)
                res_dict = {}
                for line in open(label_filepath,encoding='utf-8'):
                    res = line.strip().split('	')
                    start = int(res[1])
                    end = int(res[2])
                    label = res[3]
                    label_id = self.label_dict.get(label)
                    for i in range(start, end+1):
                        if i == start:
                            label_cate = label_id + '-B'
                        else:
                            label_cate = label_id + '-I'
                        res_dict[i] = label_cate
                for indx, char in enumerate(content):
                    char_label = res_dict.get(indx, 'O')
                    print(char,'\t',char_label)
                    f.write(char + '\t' + char_label + '\n')
        f.close()
        return



if __name__ == '__main__':
    handler = TransferData()
    train_datas = handler.transfer()

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