大数据环境下并行患者就诊时间预测算法及其在医院预约推荐中的应用
A Parallel Patient Treatment Time Prediction Algorithm and its Applications in Hospital Queuing-Recommendation in a Big Data Environment
摘要：
有效的病人排队管理，以尽量减少病人等待延误和病人过度拥挤是医院面临的主要挑战之一。长时间的不必要和令人烦恼的等待导致大量的人力资源和时间浪费，并增加患者所承受的挫折感。对于队列中的每个患者，在他之前的所有患者的总治疗时间是他必须等待的时间。如果患者可以通过实时更新的移动应用程序接收最有效的治疗计划并知道预测的等待时间，那将是方便和优选的。因此，我们提出了一个病人治疗时间预测（PTTP）算法来预测病人的每个治疗任务的等待时间。我们使用来自不同医院的真实患者数据来获得每个任务的患者治疗时间模型。基于这个大规模的真实数据集，预测每个任务的当前队列中每个患者的治疗时间。基于预测的等待时间，一个医院预约推荐（HQR）系统的开发。HQR计算并预测为患者推荐的有效和方便的治疗计划。由于大规模、真实的数据集和对实时响应的要求，PTTP算法和HQR系统要求效率和低延迟响应。我们在长沙国家超级计算中心（NSCC）使用基于Apache Spark的云实现来实现上述目标。大量的实验和仿真结果表明，我们提出的模型的有效性和适用性，建议一个有效的治疗计划，病人，以尽量减少他们在医院的等待时间。
英文摘要：
Effective patient queue management to minimize patient wait delays and patient overcrowding is one of the major challenges faced by hospitals. Unnecessary and annoying waits for long periods result in substantial human resource and time wastage and increase the frustration endured by patients. For each patient in the queue, the total treatment time of all patients before him is the time that he must wait. It would be convenient and preferable if the patients could receive the most efficient treatment plan and know the predicted waiting time through a mobile application that updates in real-time. Therefore, we propose a Patient Treatment Time Prediction (PTTP) algorithm to predict the waiting time for each treatment task for a patient. We use realistic patient data from various hospitals to obtain a patient treatment time model for each task. Based on this large-scale, realistic dataset, the treatment time for each patient in the current queue of each task is predicted. Based on the predicted waiting time, a Hospital Queuing-Recommendation (HQR) system is developed. HQR calculates and predicts an efficient and convenient treatment plan recommended for the patient. Because of the large-scale, realistic dataset and the requirement for real-time response, the PTTP algorithm and HQR system mandate efficiency and low-latency response. We use an Apache Spark-based cloud implementation at the National Supercomputing Center in Changsha (NSCC) to achieve the aforementioned goals. Extensive experimentation and simulation results demonstrate the effectiveness and applicability of our proposed model to recommend an effective treatment plan for patients to minimize their wait times in hospitals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
