湖州中医院抑郁症特效处方判定数据

通过治疗抑郁症处方数据，可助力医院构建治疗抑郁症处方疗效评估体系，通过分析历年处方使用频率及对应治疗效果，确定特效处方并纳入临床诊疗指南，为医生提供精准用药参考以提升诊疗效率；同时能为患者提供对症的最优处方信息，辅助其更清晰地了解治疗方案，还可支撑中医药科研团队开展相关处方的深度研究，推动中药的创新发展，也能为医保部门的用药报销政策制定提供数据依据，实现医疗资源的优化配置。1.数据采集：采集电子病历系统相关数据。 2.数据预处理：对湖州中医院历年抑郁症处方原始数据进行清洗，剔除重复、残缺及不符合规范的记录，统一处方中中药名称、剂量单位等关键信息的格式，对处方ID、病患关键信息等进行脱敏处理，建立标准化数据库。 3.核心算法运算：第一步通过频次统计算法，计算各处方在不同年份、不同年龄段患者群体中的使用频率，生成基础频率矩阵；第二步疗效关联算法，将处方使用记录与对应患者的治疗效果进行关联分析；第三步运用加权评分算法，P=0.4*频率（F）*500+0.6*疗效（E），对每个处方进行综合评分，筛选出评分排名前10%的处方作为候选特效处方。Leveraging prescription data for depression treatment, this dataset enables hospitals to build a therapeutic efficacy evaluation system for depression prescriptions. By analyzing annual prescription usage frequencies and their corresponding treatment outcomes, it identifies effective prescriptions and incorporates them into clinical practice guidelines, providing clinicians with precise medication references to enhance diagnostic and therapeutic efficiency. Meanwhile, it provides patients with optimal prescription information tailored to their conditions, helping them gain a clearer understanding of their treatment plans. Additionally, it supports traditional Chinese medicine (TCM) research teams in conducting in-depth studies on relevant prescriptions, promoting the innovative development of TCM, and provides data support for medical insurance departments to formulate medication reimbursement policies, thereby realizing optimal allocation of medical resources. 1. Data Collection: Collect relevant data from electronic medical record (EMR) systems. 2. Data Preprocessing: Clean the raw historical depression prescription data from Huzhou Hospital of Traditional Chinese Medicine, remove duplicate, incomplete and non-compliant records, unify the formats of key information such as TCM names and dosage units in prescriptions, conduct desensitization processing on prescription IDs and key patient information, and establish a standardized database. 3. Core Algorithm Operations: Step 1: Adopt a frequency statistics algorithm to calculate the usage frequency of each prescription among patient groups of different age groups and in different years, generating a basic frequency matrix; Step 2: Efficacy Correlation Algorithm: Perform correlation analysis between prescription administration records and the corresponding treatment outcomes of patients; Step 3: Apply a weighted scoring algorithm: P=0.4*frequency(F)*500+0.6*efficacy(E), to conduct comprehensive scoring for each prescription, and screen out prescriptions ranked in the top 10% of scores as candidate effective prescriptions.