With the development of information technology, ambient intelligence has been combined with various application areas so as to create new convergence service industries. Through IT convergence, human-oriented technologies for improving people’s quality of life has continued to be developed. Healthcare service that has been provided along with the development of various smart IT devices makes it possible to realize more efficient healthcare of people. Therefore, along with such a medical service, the advanced lifecare service for physical and mental health has been demanded. In order to meet the healthcare demands, an advanced healthcare platform has been developed. Lifecare service has been expanded to healthcare, the disease with the highest mortality induced by complications so that the service for disease survivals have been offered. Accordingly, a big-data integration and advanced healthcare platform based on patients’ life logs are developed in order for health service. In this platform, it is possible to establish an optimized model with the knowledge base and predict diseases and complications and judge a degree of risk with the use of information filtering. The conventional filtering based on a data model using scatter life logs makes use of user attribute information only for clustering so that it has low accuracy. Also, in calculating the similarity of actual users, such a method does not apply social relationships. Therefore, this study proposes a social mining based cluster process for big-data integration. The proposed method uses conventional static model information and the information extracted from the social network in order to create reliable user modeling and applies a different level of weight depending on users’ relations. In the clustering process for disease survivals’ health conditions, it is possible to predict their health risk. Based on the risk and expectation of healthcare event occurrence, their health conditions can be improved. Lifecare forecasting model that uses social relation performs social sequence mining using PrefixSpan to complement the weak point that spends a long time to scan it repeatedly in the candidate pattern. For performance evaluation, the social mining based cluster process was compared with a conventional cluster method. More specifically, the estimation accuracy of the conventional model-based cluster method was compared with the accuracy of the social mining based cluster process. As a result, the proposed method in the mining-based healthcare platform had better performance than the conventional model-based cluster method.

随着信息技术的发展，环境智能已与各种应用领域相结合，从而创建了新的融合服务行业。通过IT融合，用于改善人们生活质量的以人为本的技术继续得到发展。随着各种智能IT设备的发展而提供的医疗保健服务使人们能够实现更有效的医疗保健。因此，除了这种医疗服务之外，还需要用于身心健康的高级生命护理服务。为了满足医疗保健需求，已经开发了先进的医疗保健平台。生命护理服务已经扩展到医疗保健领域，该疾病是由并发症引起的死亡率最高的疾病，因此已经为疾病的生存提供了服务。因此，开发了基于患者生活日志的大数据集成和高级医疗平台，以提供健康服务。在这个平台上，可以使用知识库建立优化的模型，并通过信息过滤来预测疾病和并发症并判断风险程度。基于使用分散寿命日志的数据模型的常规过滤仅将用户属性信息用于聚类，因此准确性较低。另外，在计算实际用户的相似度时，这种方法不应用社交关系。因此，本研究提出了一种基于社交挖掘的大数据集成集群过程。所提出的方法使用常规的静态模型信息和从社交网络中提取的信息，以创建可靠的用户建模并根据用户的关系施加不同级别的权重。在疾病幸存者健康状况的聚类过程中，可以预测其健康风险。根据医疗事件发生的风险和期望，可以改善他们的健康状况。使用社交关系的生活预测模型使用PrefixSpan进行社交序列挖掘，以补充花费很长的时间以候选模式重复扫描的薄弱环节。为了进行绩效评估，将基于社交挖掘的聚类过程与常规聚类方法进行了比较。进一步来说，将传统的基于模型的聚类方法的估计准确性与基于社交挖掘的聚类过程的准确性进行了比较。结果，与传统的基于模型的聚类方法相比，该方法在基于挖掘的医疗保健平台中具有更好的性能。