Accurate estimates of medication rankings and volumes can benefit patients, physicians, online health communities, pharmaceutical companies, and the healthcare industry at large. This study analyzes patient-generated content in online health communities to discover important medication transition and combination patterns for better ranking and volume predictions. The current research takes a data-driven analytics approach to identify medication information from patient posts and classify different types of medication relations. The identified relation patterns then are represented in a medication relation network with an adjusted transition model for ranking and volume estimates. Evaluation results of real-world patient posts show the proposed method is more effective for predicting medication rankings than existing network-based measures. Moreover, a regression-based model, informed by the proposed method’s network-based outcomes, attains promising accuracy in estimating medication volumes, as revealed by the relatively low mean squared errors. Overall, the proposed method is capable of identifying important features for increased predictive power, as manifested by \({\text{R}}^{2}\) and adjusted \({\text{R}}^{2}\) values. It has the potential for better medication ranking and volume predictions, and offers insights for decision making by different stakeholders. This method is generalizable and can be applied in important prediction tasks in healthcare and other domains.

准确估计药物排名和数量可以使患者、医生、在线健康社区、制药公司和整个医疗保健行业受益。这项研究分析了在线健康社区中患者生成的内容，以发现重要的药物转换和组合模式，以实现更好的排名和数量预测。当前的研究采用数据驱动的分析方法来识别患者帖子中的药物信息并对不同类型的药物关系进行分类。然后，所识别的关系模式在药物关系网络中表示，并具有用于排名和数量估计的调整后的转换模型。对现实世界患者帖子的评估结果表明，所提出的方法比现有的基于网络的措施更能有效地预测药物排名。此外，基于回归的模型，根据所提出的方法的基于网络的结果，在估计药物量方面获得了有希望的准确性，正如相对较低的均方误差所揭示的那样。总体而言，所提出的方法能够识别重要特征以提高预测能力，如\({\text{R}}^{2}\)和调整后的\({\text{R}}^{2}\ ）值。它有可能提供更好的药物排名和数量预测，并为不同利益相关者的决策提供见解。该方法具有普适性，可应用于医疗保健和其他领域的重要预测任务。