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基于大數據的北京市醫保藥品數據分析系統-功能介紹

基于大數據的北京市醫保藥品數據分析系統是一款專門針對首都地區醫保藥品目錄進行深度數據挖掘和智能分析的綜合性平臺。該系統采用Hadoop+Spark大數據技術架構作為核心引擎，結合Django后端框架和Vue前端技術，構建了從數據存儲、處理到可視化展示的完整技術鏈路。系統主要圍繞藥品核心屬性分布、生產企業市場格局、醫保報銷限制策略、中藥配方顆粒專題以及基于機器學習算法的藥品關聯聚類等五個核心維度展開分析，通過Spark SQL進行大規模數據查詢和統計計算，運用Pandas、NumPy等Python科學計算庫實現復雜的數據處理邏輯，并借助Echarts圖表庫將分析結果以直觀的可視化圖表形式呈現給用戶。整個系統不僅能夠處理海量的醫保藥品數據，還能挖掘出藥品屬性間的潛在關聯規律，為醫保政策制定、藥企市場分析和患者用藥指導提供數據支撐，展現了大數據技術在醫療保障領域的實際應用價值。

基于大數據的北京市醫保藥品數據分析系統-選題背景意義

選題背景
隨著我國醫療衛生體制改革的深入推進和人口老齡化趨勢的加速發展，醫療保障制度作為社會保障體系的重要組成部分，其管理的復雜性和數據量呈現出爆炸式增長態勢。北京市作為國家首都和醫療資源最為集中的超大型城市，其醫保藥品目錄涵蓋了數萬種不同類型、不同廠商生產的藥品，這些藥品在醫保等級、報銷比例、使用限制等方面存在著復雜的分類和管理規則。傳統的數據分析方法已經難以應對如此龐大且多維度的藥品數據處理需求，醫保管理部門迫切需要借助大數據技術來深入挖掘藥品數據中蘊含的規律和趨勢。與此同時，藥品生產企業也需要通過數據分析來了解自身產品在醫保市場中的競爭地位，患者和醫療機構則希望能夠更清晰地掌握不同藥品的報銷政策和使用限制。在這樣的背景下，構建一個基于大數據技術的醫保藥品數據分析系統顯得尤為重要和迫切。
選題意義
本課題的研究和實現具有多重現實意義，能夠在一定程度上為相關領域提供有益的技術探索和應用參考。從技術角度來看，該系統將Hadoop分布式存儲、Spark大數據計算引擎與傳統Web開發技術有機結合，為大數據技術在醫療保障領域的應用提供了一個具體的實踐案例，有助于驗證大數據技術處理復雜醫療數據的可行性和有效性。從應用角度來說，系統通過對醫保藥品數據的多維度分析，能夠為醫保管理部門提供一些數據支撐，幫助其更好地了解藥品目錄的構成特征和潛在問題，為政策優化提供參考依據。對于藥品生產企業而言，系統分析的市場競爭格局和產品布局策略信息，可以在一定程度上幫助企業了解自身在醫保市場中的地位。對于醫療機構和患者來說，系統提供的藥品報銷限制和使用條件分析，能夠為合理用藥和就醫選擇提供一些信息參考。當然，作為一個畢業設計項目，本系統的主要價值還是在于技術學習和實踐探索，為大數據技術的進一步應用積累經驗。

基于大數據的北京市醫保藥品數據分析系統-技術選型

大數據框架：Hadoop+Spark（本次沒用Hive，支持定制）
開發語言：Python+Java（兩個版本都支持）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（兩個版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
詳細技術點：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
數據庫：MySQL

基于大數據的北京市醫保藥品數據分析系統-視頻展示

基于大數據的北京市醫保藥品數據分析系統-圖片展示

基于大數據的北京市醫保藥品數據分析系統-代碼展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, desc, when, regexp_extract
from pyspark.ml.feature import VectorAssembler, StandardScaler
from pyspark.ml.clustering import KMeans
from django.http import JsonResponse
import pandas as pd
import numpy as np
from collections import Counter
import respark = SparkSession.builder.appName("MedicalInsuranceDrugAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()def drug_core_attribute_analysis(request):df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/medical_insurance").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "drug_info").option("user", "root").option("password", "123456").load()insurance_level_stats = df.groupBy("medical_insurance_level").agg(count("*").alias("drug_count"), avg("self_payment_ratio").alias("avg_self_payment")).orderBy(desc("drug_count"))insurance_level_result = insurance_level_stats.collect()level_distribution = {}for row in insurance_level_result:level_distribution[row['medical_insurance_level']] = {'count': row['drug_count'], 'avg_self_payment': float(row['avg_self_payment']) if row['avg_self_payment'] else 0}dosage_form_stats = df.groupBy("drug_dosage_form").agg(count("*").alias("form_count")).orderBy(desc("form_count")).limit(20)dosage_form_result = dosage_form_stats.collect()form_distribution = {row['drug_dosage_form']: row['form_count'] for row in dosage_form_result}fixed_ratio_drugs = df.filter(col("fixed_ratio_payment_flag") == "是").count()total_drugs = df.count()fixed_ratio_percentage = (fixed_ratio_drugs / total_drugs) * 100 if total_drugs > 0 else 0high_frequency_drugs = df.groupBy("registration_name").agg(count("*").alias("frequency")).orderBy(desc("frequency")).limit(30)frequency_result = high_frequency_drugs.collect()high_freq_list = [{'drug_name': row['registration_name'], 'frequency': row['frequency']} for row in frequency_result]self_payment_analysis = df.groupBy("medical_insurance_level").agg(avg("self_payment_ratio").alias("avg_ratio"), count("*").alias("drug_count")).collect()payment_analysis = {row['medical_insurance_level']: {'avg_ratio': float(row['avg_ratio']) if row['avg_ratio'] else 0, 'count': row['drug_count']} for row in self_payment_analysis}result_data = {'level_distribution': level_distribution, 'form_distribution': form_distribution, 'fixed_ratio_percentage': round(fixed_ratio_percentage, 2), 'high_frequency_drugs': high_freq_list, 'payment_analysis': payment_analysis}return JsonResponse({'status': 'success', 'data': result_data})def enterprise_market_analysis(request):df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/medical_insurance").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "drug_info").option("user", "root").option("password", "123456").load()enterprise_filtered = df.filter(col("manufacturer_name") != "無").filter(col("manufacturer_name").isNotNull())market_share_stats = enterprise_filtered.groupBy("manufacturer_name").agg(count("*").alias("drug_count")).orderBy(desc("drug_count")).limit(20)market_share_result = market_share_stats.collect()enterprise_ranking = [{'enterprise_name': row['manufacturer_name'], 'drug_count': row['drug_count']} for row in market_share_result]top_enterprises = [row['manufacturer_name'] for row in market_share_result[:10]]top_enterprise_filter = enterprise_filtered.filter(col("manufacturer_name").isin(top_enterprises))product_strategy = top_enterprise_filter.groupBy("manufacturer_name", "medical_insurance_level").agg(count("*").alias("level_count")).collect()strategy_analysis = {}for row in product_strategy:enterprise = row['manufacturer_name']if enterprise not in strategy_analysis:strategy_analysis[enterprise] = {}strategy_analysis[enterprise][row['medical_insurance_level']] = row['level_count']dosage_specialization = top_enterprise_filter.groupBy("manufacturer_name", "drug_dosage_form").agg(count("*").alias("form_count")).collect()specialization_analysis = {}for row in dosage_specialization:enterprise = row['manufacturer_name']if enterprise not in specialization_analysis:specialization_analysis[enterprise] = {}specialization_analysis[enterprise][row['drug_dosage_form']] = row['form_count']missing_manufacturer = df.filter((col("manufacturer_name") == "無") | col("manufacturer_name").isNull()).count()total_drugs = df.count()missing_percentage = (missing_manufacturer / total_drugs) * 100 if total_drugs > 0 else 0market_concentration = sum([row['drug_count'] for row in market_share_result[:5]]) / total_drugs * 100 if total_drugs > 0 else 0result_data = {'enterprise_ranking': enterprise_ranking, 'product_strategy': strategy_analysis, 'dosage_specialization': specialization_analysis, 'missing_manufacturer_percentage': round(missing_percentage, 2), 'market_concentration_top5': round(market_concentration, 2)}return JsonResponse({'status': 'success', 'data': result_data})def reimbursement_restriction_analysis(request):df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/medical_insurance").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "drug_info").option("user", "root").option("password", "123456").load()restriction_df = df.select("reimbursement_restriction", "medical_insurance_level", "drug_dosage_form").filter(col("reimbursement_restriction").isNotNull())hospital_restricted_pattern = r"僅限(.+?)使用"hospital_restricted = restriction_df.filter(col("reimbursement_restriction").rlike("僅限.+使用"))hospital_names = hospital_restricted.select(regexp_extract(col("reimbursement_restriction"), hospital_restricted_pattern, 1).alias("hospital_name")).filter(col("hospital_name") != "")hospital_stats = hospital_names.groupBy("hospital_name").agg(count("*").alias("drug_count")).orderBy(desc("drug_count")).limit(15)hospital_result = hospital_stats.collect()hospital_distribution = [{'hospital_name': row['hospital_name'], 'drug_count': row['drug_count']} for row in hospital_result]restriction_categories = restriction_df.withColumn("restriction_type", when(col("reimbursement_restriction").rlike("僅限"), "指定醫院使用").when(col("reimbursement_restriction").rlike("工傷保險"), "工傷保險支付").when(col("reimbursement_restriction").rlike("無限制"), "無限制").otherwise("其他限制"))restriction_stats = restriction_categories.groupBy("restriction_type").agg(count("*").alias("count")).collect()restriction_distribution = {row['restriction_type']: row['count'] for row in restriction_stats}level_restriction = restriction_categories.groupBy("medical_insurance_level", "restriction_type").agg(count("*").alias("count")).collect()level_restriction_analysis = {}for row in level_restriction:level = row['medical_insurance_level']if level not in level_restriction_analysis:level_restriction_analysis[level] = {}level_restriction_analysis[level][row['restriction_type']] = row['count']dosage_restriction = restriction_categories.groupBy("drug_dosage_form", "restriction_type").agg(count("*").alias("count")).collect()dosage_restriction_analysis = {}for row in dosage_restriction:dosage = row['drug_dosage_form']if dosage not in dosage_restriction_analysis:dosage_restriction_analysis[dosage] = {}dosage_restriction_analysis[dosage][row['restriction_type']] = row['count']total_restricted = restriction_df.filter(~col("reimbursement_restriction").rlike("無限制")).count()total_drugs = restriction_df.count()restriction_ratio = (total_restricted / total_drugs) * 100 if total_drugs > 0 else 0result_data = {'hospital_distribution': hospital_distribution, 'restriction_type_distribution': restriction_distribution, 'level_restriction_analysis': level_restriction_analysis, 'dosage_restriction_analysis': dosage_restriction_analysis, 'overall_restriction_ratio': round(restriction_ratio, 2)}return JsonResponse({'status': 'success', 'data': result_data})

基于大數據的北京市醫保藥品數據分析系統-結語

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