牡蛎中医药品季度使用量预测模型数据

通过分析桐乡市医疗机构季度牡蛎中药材使用量，可以预测下个季度牡蛎中药材的使用量，从而合理安排库存，避免库存积压或短缺。这有助于减少药品过期浪费，通过对医疗机构每季度中药材使用量的分析，可以了解不同地区、不同医疗机构之间中药材使用情况的差异。这有助于政府和相关部门在制定医疗资源配置政策时更加科学合理，促进医疗资源的合理配置和有效利用。
1. 数据采集：收集医疗机构的牡蛎中药材的季度库存和使用数据，包括季度入库量、季度使用量等。 2. 数据预处理：对中药材的库存和使用数据进行清洗，去除异常值，平滑数据，以确保数据的准确性和可用性。 3. 特征工程：生成基于时间的特征，季节性变化系数SC(t)，表示第t季度的使用量变化；生成计算库存周转率ITR(t) = Total_Incoming(t) / Total_Used(t)，其中Total_Incoming(t)是第t季度的入库总量，Total_Used(t)是第t季度的使用总量。 4. 季节性模型构建： 季度模型： S（t）=ω1 * U(t-1) + ω2* U(t-2)+…+β1 * SC（t） +β2 * ITR(t)，其中U(t-k)代表第t-k季度的实际使用量，ω1和ω2是时间序列权重，β1和β2是特征权重。 季度的模型考虑了季节性影响和历史使用数据，以及库存周转率，确保了预测的精确性和适用性。通过这种方式，医疗机构可以更精准地预测和调整中药材的库存，以保障医疗服务的连续性和效率。

Analyzing the quarterly usage volume of oyster medicinal materials in medical institutions of Tongxiang City enables prediction of the next quarter's usage volume, allowing for rational inventory arrangement to avoid overstock or stock shortage. This measure helps reduce waste caused by expired pharmaceutical products. Additionally, analyzing the quarterly traditional Chinese medicinal (TCM) usage data of medical institutions allows identification of disparities in TCM usage across different regions and medical institutions, which supports government and relevant departments in formulating more scientific and evidence-based medical resource allocation policies, thereby promoting rational allocation and efficient utilization of medical resources.

1. Data Collection: Collect quarterly inventory and usage data of oyster medicinal materials from medical institutions, including total quarterly incoming stock and total quarterly usage volume.
2. Data Preprocessing: Clean the collected inventory and usage data of TCMs, remove outliers, and perform data smoothing to ensure the accuracy and usability of the dataset.
3. Feature Engineering: Generate time-based features: the seasonal variation coefficient SC(t), which denotes the change in usage volume during the t-th quarter; calculate the inventory turnover rate ITR(t) = Total_Incoming(t) / Total_Used(t), where Total_Incoming(t) refers to the total incoming stock volume in the t-th quarter, and Total_Used(t) refers to the total usage volume in the t-th quarter.
4. Seasonal Model Construction:
Quarterly Model: S(t) = ω₁ * U(t-1) + ω₂ * U(t-2) + … + β₁ * SC(t) + β₂ * ITR(t)
Here, U(t-k) represents the actual usage volume in the (t-k)-th quarter, ω₁ and ω₂ are time series weights, while β₁ and β₂ are feature weights.
The quarterly model incorporates seasonal effects, historical usage data, and inventory turnover rate, ensuring the accuracy and applicability of the prediction. With this model, medical institutions can accurately predict and adjust the inventory of TCMs, thus guaranteeing the continuity and efficiency of medical services.

搜集汇总

数据集介绍

特点

该数据集为桐乡市医疗机构的中医药品季度使用量数据，包含14992条记录，每季度更新，用于预测下季度药品使用量以优化库存管理。数据字段涵盖药品名、使用量、库存周转率等关键信息，应用季节性模型进行预测分析。