根据预后标志物的相似性推荐最佳治疗方案
Recommending best course of treatment based on similarities of prognostic markers
摘要：
随着技术领域的进步跨越各个领域，大量信息的涌入是不可避免的。在技术进步带来的所有机会中，其中之一就是提出有效的数据检索解决方案。这意味着从大量的数据中，检索方法应该允许用户随着时间的推移获取相关的和最近的数据。在娱乐和电子商务领域中，推荐系统已经起到提供上述内容的作用。在医疗领域使用相同的系统肯定会以各种方式证明是有用的。在此背景下，本文的目标是提出基于协同过滤的推荐系统在医疗保健部门推荐补救措施的基础上所经历的症状的患者。此外，还开发了一个新的数据集，其中包括有关各种疾病的补救措施，以解决数据有限的问题。建议的推荐系统接受患者的预后指标作为输入，并生成最佳的补救过程。通过几次实验，该模型在推荐给定预后标志物的可能补救措施方面取得了令人鼓舞的结果。
英文摘要：
With the advancement in the technology sector spanning over every field, a huge influx of information is inevitable. Among all the opportunities that the advancements in the technology have brought, one of them is to propose efficient solutions for data retrieval. This means that from an enormous pile of data, the retrieval methods should allow the users to fetch the relevant and recent data over time. In the field of entertainment and e-commerce, recommender systems have been functioning to provide the aforementioned. Employing the same systems in the medical domain could definitely prove to be useful in variety of ways. Following this context, the goal of this paper is to propose collaborative filtering based recommender system in the healthcare sector to recommend remedies based on the symptoms experienced by the patients. Furthermore, a new dataset is developed consisting of remedies concerning various diseases to address the limited availability of the data. The proposed recommender system accepts the prognostic markers of a patient as the input and generates the best remedy course. With several experimental trials, the proposed model achieved promising results in recommending the possible remedy for given prognostic markers.
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。
