Department of Software Engineering, University of Engineering and Technology Taxila, Pakistan.
Department of Software Engineering, University of Engineering and Technology Taxila, Pakistan.
Artif Intell Med. 2020 Jan;102:101761. doi: 10.1016/j.artmed.2019.101761. Epub 2019 Nov 16.
In the last few years, hospitals have been collecting a large amount of health related digital data for patients. This includes clinical test reports, treatment updates and disease diagnosis. The information extracted from this data is used for clinical decisions and treatment recommendations. Among health recommender systems, collaborative filtering technique has gained a significant success. However, traditional collaborative filtering algorithms are facing challenges such as data sparsity and scalability, which leads to a reduction in system accuracy and efficiency. In a clinical setting, the recommendations should be accurate and timely. In this paper, an improvised collaborative filtering technique is proposed, which is based on clustering and sub-clustering. The proposed methodology is applied on a supervised set of data for four different types of cardiovascular diseases including angina, non-cardiac chest pain, silent ischemia, and myocardial infarction. The patient data is partitioned with respect to their corresponding disease class, which is followed by k-mean clustering, applied separately on each disease partition. A query patient once directed to the correct disease partition requires to get similarity scores from a reduced sub-cluster, thereby improving the efficiency of the system. Each disease partition has a separate process for recommendation, which gives rise to modularization and helps in improving scalability of the system. The experimental results demonstrate that the proposed modular clustering based recommender system reduces the spatial search domain for a query patient and the time required for providing accurate recommendations. The proposed system improves upon the accuracy of recommendations as demonstrated by the precision and recall values. This is significant for health recommendation systems particularly for those related to cardiovascular diseases.
在过去的几年中，医院一直在为患者收集大量与健康相关的数字数据。这些数据包括临床测试报告、治疗更新和疾病诊断。从这些数据中提取的信息用于临床决策和治疗建议。在健康推荐系统中，协同过滤技术取得了显著的成功。然而，传统的协同过滤算法面临着数据稀疏和可扩展性等挑战，这导致系统的准确性和效率降低。在临床环境中，推荐应该是准确和及时的。在本文中，提出了一种基于聚类和子聚类的改进协同过滤技术。该方法应用于包括心绞痛、非心源性胸痛、无症状性心肌缺血和心肌梗死在内的四种不同类型心血管疾病的监督数据集。根据患者的疾病类型对患者数据进行分区，然后对每个疾病分区分别应用 k-均值聚类。一旦将查询患者定向到正确的疾病分区，就需要从缩小的子聚类中获取相似性得分，从而提高系统的效率。每个疾病分区都有单独的推荐过程，这实现了模块化，并有助于提高系统的可扩展性。实验结果表明，所提出的基于模块化聚类的推荐系统减少了查询患者的空间搜索域，并减少了提供准确推荐所需的时间。所提出的系统通过精度和召回值提高了推荐的准确性。这对于健康推荐系统，特别是与心血管疾病相关的推荐系统非常重要。
