红花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红花中药材使用量，可以预测下个季度红花中药材的使用量，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：收集医疗机构的花红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。

By analyzing the quarterly usage volume of safflower Chinese medicinal materials in medical institutions of Tongxiang City, the usage volume of safflower Chinese medicinal materials in the next quarter can be predicted, so as to optimize inventory arrangements and avoid overstocking or stockouts. This helps reduce expired drug waste. Additionally, analyzing the quarterly usage data of Chinese medicinal materials in medical institutions can identify disparities in herbal medicine usage across different regions and medical institutions, which assists governments and relevant departments in formulating more scientific and rational medical resource allocation policies, thereby promoting the rational allocation and efficient utilization of medical resources.

1. Data Collection: Collect quarterly inventory and usage data of safflower Chinese medicinal materials from medical institutions, including quarterly incoming stock volume and quarterly usage volume.
2. Data Preprocessing: Clean the inventory and usage data of Chinese medicinal materials, remove outliers, and smooth the data to ensure the accuracy and availability of the dataset.
3. Feature Engineering: Generate time-based features, including the seasonal variation coefficient SC(t), which represents the change in usage volume in quarter t; calculate the inventory turnover ratio ITR(t) = Total_Incoming(t) / Total_Used(t), where Total_Incoming(t) is the total incoming stock in quarter t, and Total_Used(t) is the total usage volume in quarter t.
4. Seasonal Model Construction:
Quarterly model: $S(t) = omega_1 cdot U(t-1) + omega_2 cdot U(t-2) + dots + eta_1 cdot SC(t) + eta_2 cdot ITR(t)$, where $U(t-k)$ represents the actual usage volume in quarter $t-k$, $omega_1$ and $omega_2$ are time series weights, and $eta_1$ and $eta_2$ are feature weights.
The quarterly model takes into account seasonal effects, historical usage data, and inventory turnover ratio, ensuring the accuracy and applicability of the prediction. Through this approach, medical institutions can accurately predict and adjust the inventory of Chinese medicinal materials, thus guaranteeing the continuity and efficiency of medical services.

搜集汇总

数据集介绍

特点

该数据集包含桐乡市医疗机构红花中药材的季度使用数据，用于预测下个季度使用量以优化库存管理。数据规模为14992条，每季度更新，应用场景包括减少药品浪费和促进医疗资源合理配置。算法模型考虑了季节性影响和历史使用数据，确保预测的精确性和适用性。