Evaluation Of Multivariate Data Acquisition Of Network Embedding Scheme For Healthcare Applications
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التاريخ
عنوان الدورية
ردمد الدورية
عنوان المجلد
الناشر
Journal of Theoretical and Applied Information Technology
خلاصة
In recent years, recommendation systems have evolved to provide valuable information with respect to a multitude of domains. In the ongoing medical revolution, they have been widely utilized for identifying trends from electronic record data. Techniques were developed to compute the correlation between patients suffering from diseases with similar symptoms, identification of treatment procedures and drug identification. However, the feasibility of the heterogeneous network embedding schemes needs to be studied in the dimension of varying input data formats and the corresponding performance. In the following work, emphasis is placed on understanding the impact of a variety of data, volume and the number of recommendations produced by the system. Further, the metrics such as precision and recall were utilized to evaluate the overall performance of the recommendation system, which is built upon Metapath2Vec. The current study extrapolates the effectiveness of the recommendation system using data gathered from 1500 influenza patients, further elucidating the ability of recommendation systems to identify distinct trends from the disease's symptoms that are perceptible to people. In addition to the implementation of Metapath2Vec and corresponding analysis, a detailed note is provided on elucidating the future of network embedding schemes and recommendation systems