吴裕雄 python 人工智能——智能医疗系统后台用户复诊模块简约版代码展示

#复诊

import sys

import os

import time

import operator

import cx_Oracle

import numpy as np

import pandas as pd

import tensorflow as tf

uid = 6

sql = "select username,sex,age,province,area,bumen,ke,result,chufang,jianyi,yiyuaan,yisheng,jianchaxiang,zhenduanriqi from zhenduanjilutable where userid=%d" % uid

cursor.execute(sql)

rows = cursor.fetchall()

zhenduanjilu = []

for row in rows:

    temp = []

    temp.append(row[0])

    temp.append(row[1])

    temp.append(row[2])

    temp.append(row[3])

    temp.append(row[4])

    temp.append(row[5])

    temp.append(row[6])

    temp.append(row[7])

    temp.append(row[8])

    temp.append(row[9])

    temp.append(row[10])

    temp.append(row[11])

    temp.append(row[12])

    temp.append(row[13])

    zhenduanjilu.append(temp)

print("===================打印诊断历史记录=====================")

for i in range(len(zhenduanjilu)):

    print("-------------->>第："+str(i+1)+"次诊断<<-------------------")

    print("     姓名："+zhenduanjilu[i][0])

    print("     性别："+zhenduanjilu[i][1])

    print("     年龄："+str(zhenduanjilu[i][2]))

    print("     省份："+zhenduanjilu[i][3])

    print("     市区："+zhenduanjilu[i][4])

    print("     门诊部门："+zhenduanjilu[i][5])

    print("     门诊科目："+zhenduanjilu[i][6])

    print("     诊断结果："+zhenduanjilu[i][7])

    print("     医疗处方："+zhenduanjilu[i][8])

    print("     养生建议："+zhenduanjilu[i][9])

    print("     推荐医院："+zhenduanjilu[i][10])

    print("     推荐医生："+zhenduanjilu[i][11])

    print("     建议检查项："+zhenduanjilu[i][12])

    print("     诊断日期："+str(zhenduanjilu[i][13]))

num = int(input("请输入需要复诊的对应第几次诊断记录编号："))

print("===================开始复诊流程=====================")

num -= 1

username=zhenduanjilu[num][0]

sex=zhenduanjilu[num][1]

age=zhenduanjilu[num][2]

province=zhenduanjilu[num][3]

area=zhenduanjilu[num][4]

bumen=zhenduanjilu[num][5]

ke=zhenduanjilu[num][6]

sql = "select keid from hy_keid where ke='%s'" % ke

cursor.execute(sql)

rows = cursor.fetchall()

keid = []

for row in rows:

    keid.append(row[0])

sql = "select QUESTION from HY_QUESTID where QUID=%d" % keid[0]

cursor.execute(sql)

rows = cursor.fetchall()

question = []

for row in rows:

    question.append(row[0])

question = question[0].split(",")

# print(question)

answer = []

for i,j in zip(question,np.arange(len(question))):

    print("问题"+str(j+1)+"：是否"+i+"：")

    print("    A、正常    B、较轻    C、明显    D、非常严重")

    temp = input("请根据实际情况选择上面的一项：")

    if(temp=="A"):

        answer.append(1)

    elif(temp=="B"):

        answer.append(2)

    elif(temp=="C"):

        answer.append(3)

    else:

        answer.append(4)

surgery = bumen

surgeryChest = ke

#one-hot编码

def onehot(labels):

    n_sample = len(labels)

    n_class = max(labels) + 1

    onehot_labels = np.zeros((n_sample, n_class))

    onehot_labels[np.arange(n_sample), labels] = 1

    return onehot_labels

#获取数据集

def getdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from menzhen where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    dataset = []

    lables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        dataset.append(temp)

        if(row[5]==3):

            lables.append(0)

        elif(row[5]==6):

            lables.append(1)

        else:

            lables.append(2)

    dataset = np.array(dataset)

    lables = np.array(lables)

    dataset = dataset.astype(np.float32)

    labless = onehot(lables)

    return dataset,labless

#获取测试数据集

def gettestdata(surgery,surgeryChest):

    sql = "select feature1,feature2,feature3,feature4,feature5,trainLable from test where surgery='%s' and surgeryChest='%s'" % (surgery,surgeryChest)

    cursor.execute(sql)

    rows = cursor.fetchall()

    testdataset = []

    testlables = []

    for row in rows:

        temp = []

        temp.append(row[0])

        temp.append(row[1])

        temp.append(row[2])

        temp.append(row[3])

        temp.append(row[4])

        testdataset.append(temp)

        if(row[5]==3):

            testlables.append(0)

        elif(row[5]==6):

            testlables.append(1)

        else:

            testlables.append(2)

    testdataset = np.array(testdataset)

    testlables = np.array(testlables)

    testdataset = testdataset.astype(np.float32)

    testlabless = onehot(testlables)

    return testdataset,testlabless

dataset,labless = getdata(surgery,surgeryChest)

# testdataset,testlables = gettestdata(surgery,surgeryChest)

# dataset = dataset[0:100]

# labless = labless[0:100]

x_data = tf.placeholder("float32", [None, 5])

y_data = tf.placeholder("float32", [None, 3])

weight = tf.Variable(tf.ones([5, 3]))

bias = tf.Variable(tf.ones([3]))

#使用softmax激活函数

y_model = tf.nn.softmax(tf.matmul(x_data, weight) + bias)

#y_model = tf.nn.relu(tf.matmul(x_data, weight) + bias)

# loss = tf.reduce_sum(tf.pow((y_model - y_data), 2))

#使用交叉熵作为损失函数

loss = -tf.reduce_sum(y_data*tf.log(y_model))

# train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(loss)

#使用AdamOptimizer优化器

# train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)

train_step = tf.train.MomentumOptimizer(1e-4,0.9).minimize(loss)

#评估模型

correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(y_data, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

init = tf.initialize_all_variables()

sess = tf.Session()

sess.run(init)

# start = time.time()

for _ in range(10):

    for i in range(int(len(dataset)/100)):

        sess.run(train_step, feed_dict={x_data:dataset[i:i+100,:], y_data:labless[i:i+100,:]})

# print("模型准确率",sess.run(accuracy, feed_dict={x_data:testdataset , y_data:testlables}))

# end = time.time()

# print("模型训练和测试公耗时：%.2f 秒" % (end-start))

xl_weight = sess.run(weight)

useranswer = [[1, 2, 3, 4, 4]]*3

W = np.dot(xl_weight,useranswer)

result=0

for i in range(len(W[0])):

    for j in range(len(W[0,:])):

        if(i==j):

            result += W[i][j]

result = int(result/5)

# print(result)

if(result<=3):

    result = 3

elif(result<=6):

    result = 6

else:

    result = 9

sql = "select ILL_NAME from ill_result_tbZ where FAMILY='%s' and ILL_ID=%d" % (ke,result)

cursor.execute(sql)

rows = cursor.fetchall()

ILL_NAME = []

for row in rows:

    ILL_NAME.append(row[0])

firstResult = ILL_NAME[0]

##

print("=======================系统复诊单===================")

print("姓名："+username)

print("性别："+sex)

print("年龄："+str(age))

print("省份："+province)

print("所属区："+area)

print("初诊部门："+bumen)

print("科目："+ke)

##

print("系统智能诊断结果："+firstResult)

if(firstResult[:2]=="疑似"):

    firstResult = "疑似患病"

sql = "select PRESCRIPTION_1,PRESCRIPTION_2,PRESCRIPTION_3,PRESCRIPTION_4,PRESCRIPTION_5,PRESCRIPTION_6,PRESCRIPTION_7,PRESCRIPTION_8,PRESCRIPTION_9,PRESCRIPTION_10,PRESCRIPTION_11,PRESCRIPTION_12,PRESCRIPTION_13,PRESCRIPTION_14,PRESCRIPTION_15 from PRESCRIPTION where SURGERY='%s'and SURGERYCHEST='%s' and ILLNAME='%s'" % (bumen,ke,firstResult)

cursor.execute(sql)

rows = cursor.fetchall()

chufanggrace = []

for row in rows:

    temp = []

    temp.append(row[0])

    temp.append(row[1])

    temp.append(row[2])

    temp.append(row[3])

    temp.append(row[4])

    temp.append(row[5])

    temp.append(row[6])

    temp.append(row[7])

    temp.append(row[8])

    temp.append(row[9])

    temp.append(row[10])

    temp.append(row[11])

    temp.append(row[12])

    temp.append(row[13])

    temp.append(row[14])

    chufanggrace.append(temp)

PRESCRIPTION_sum = []

for col in range(np.shape(chufanggrace)[1]):

    temp = 0

    for row in range(np.shape(chufanggrace)[0]):

        temp += chufanggrace[row][col]

    PRESCRIPTION_sum.append(temp)

b = sorted(enumerate(PRESCRIPTION_sum),key=lambda x:x[1],reverse=True)[:3]

index = []

for i in b:

    index.append(i[0])

sql = "select PRESCRIPTIONINFO,HEALTH from PRESCRIPTIONINFO where DEPARTMENT='%s' and FAMILY='%s' and ILL_NAME='%s'" % (bumen,ke,firstResult)

cursor.execute(sql)

rows = cursor.fetchall()

chufanglist = []

jianyilist = []

for row in rows:

    chufanglist.append(row[0])

    jianyilist.append(row[1])

best_chufang = []

best_jianyi = []

for i in index:

    best_chufang.append(chufanglist[i])

    best_jianyi.append(jianyilist[i])

chufang_str = ""

jianyi_str = ""

for i,j in zip(best_chufang,range(len(best_chufang))):

    chufang_str += "系统智能筛选优良处方"+str(j+1)+"：" + i +"。"

    print("系统智能筛选优良处方"+str(j+1)+"：" + i)

for i,j in zip(best_jianyi,range(len(best_jianyi))):

    jianyi_str += "系统智能筛选优良养生建议"+str(j+1)+"：" + i+"。"

    print("系统智能筛选优良养生建议"+str(j+1)+"：" + i)

sql = "select HOSTITALNAME from DOCTORHOSTITALADRREST where PROVINCE='%s' and ADMINISTRATIVE='%s'" % (province,area)

cursor.execute(sql)

rows = cursor.fetchall()

yiyuan = []

for row in rows:

    yiyuan.append(row[0])

for i in yiyuan:

    print("系统智能匹配你所在地区附件的医院："+i)

sql = "select ADDRACTION,NAME,SUMMARY from DOCTORS where FAMILY='%s'" % (ke)

cursor.execute(sql)

rows = cursor.fetchall()

yisheng = []

for row in rows:

    yisheng.append(row[0])

    yisheng.append(row[1])

    yisheng.append(row[2])

print("系统为你推荐全国相关出色医生所在医院信息："+yisheng[0])

print("系统为你推荐全国相关出色医生姓名信息："+yisheng[1])

print("系统为你推荐全国相关出色医生简介信息："+yisheng[2])

yisheng_str = ""

yisheng_str += "医生所在医院："+yisheng[0]

yisheng_str += "医生姓名："+yisheng[1]

yisheng_str += "医生简介："+yisheng[2]

sql = "select CHACKPRO from CHACKPROJECT where FAMILY='%s'" % (ke)

cursor.execute(sql)

rows = cursor.fetchall()

jiancha = []

for row in rows:

    jiancha.append(row[0])

print("系统建议你到相关正规医院检查以下身体指标："+jiancha[0])

jianchax = ""

jianchax += "系统建议你到相关正规医院检查以下身体指标："+jiancha[0]

sql = "insert into zhenduanjilutable (userid,username,sex,age,province,area,bumen,ke,result,chufang,jianyi,yiyuaan,yisheng,jianchaxiang) values (%d,'%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s')" % (uid,username,sex,age,province,area,bumen,ke,firstResult,chufang_str,jianyi_str,yiyuan[0],yisheng_str,jianchax)

cursor.execute(sql)

conn.commit()

print("此次智能诊断完成，欢迎你下次继续使用：天生自然健康智能医疗系统！")

print("特别提醒、注意：该系统的所有诊断只是作为参考，有必需要的用户请到相关正规医院接受相关专家医生完成检查、治疗等流程...")

print("系统建议：保持一颗善良、沉稳、宁静和广博的平常心度过每一个清晨和夜晚...")

print("祝你们每一位人都开开心心、健健康康、平平安安...阖家安康，如意吉祥......")