As an efficacious solution to remedying the imbalance of medical resources, the online medical platform has burgeoned expeditiously. Apt allotment of medical resources on the medical platform can facilitate patients in reasonably selecting physicians and time slots, coordinating doctors' clinical arrangements, and generating precise projections of medical platform service volume to enhance patient satisfaction and alleviate physicians' workload. To this end, grounded in the data‐driven method, this paper assembles an exhaustive feature set encompassing hospital features, physician features, and patient features. Through feature selection, appropriate features are screened, and machine learning algorithms are leveraged to accurately forecast doctors' online consultation volume. Subsequently, to glean the influence relationship between online medical services and offline medical services, this paper introduces features of offline medical services such as hospital registration volume and regional gross domestic product (GDP) to solve the prediction of offline medical service volume using online medical information. The findings signify that online data feature prediction can pinpoint superior machine learning models for online medical platform service volume (with the optimal accuracy up to 96.89%). Online features exert a positive effect on predicting offline medical service volume, but the accuracy declines to some degree (the optimal accuracy is 73%). Physicians with favorable reputations on the online platform are more susceptible to attain higher offline appointment volumes when online consultation volume is a vital feature impacting offline appointment volume.

中文翻译：

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使用机器学习算法预测医疗保健服务量

作为弥补医疗资源失衡的有效解决方案，在线医疗平台迅速发展。医疗平台医疗资源的合理配置，可以方便患者合理选择医生和时段，协调医生临床安排，精准预测医疗平台服务量，提高患者满意度，减轻医生工作量。为此，本文以数据驱动的方法为基础，汇集了包含医院特征、医生特征和患者特征的详尽特征集。通过特征选择，筛选合适的特征，利用机器学习算法准确预测医生在线问诊量。随后，为了梳理线上医疗服务与线下医疗服务之间的影响关系，引入医院挂号量、地区GDP等线下医疗服务特征，解决利用线上医疗信息对线下医疗服务量的预测。 。研究结果表明，在线数据特征预测可以为在线医疗平台服务量找到优质的机器学习模型（最佳准确率高达 96.89%）。在线特征对预测线下医疗服务量有积极作用，但准确率有所下降（最优准确率为73%）。当在线问诊量是影响线下预约量的重要因素时，在在线平台上拥有良好声誉的医生更容易获得更高的线下预约量。