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A Referral Approach to Finding Medical Informatics Reviewers

机译：寻找医学信息审稿人的推荐方法

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摘要

We proposed and investigated a referral approach to finding experts in distributed networked environments. An expert finding task, namely, finding "good" medical informatics reviewers in peer-review processes, was used in an agent-based simulation environment to evaluate the effectiveness, efficiency, and scalability of the proposed model. Experiments on a coauthorship network of 181 peers showed that the model based on topical relevance cues was able to identify experts within a very short referral chain and largely outperformed random walks. Applying heuristics to rewire the connections and to constrain the use of remote neighbors showed large improvement on efficiency and provided a better network underpinning for finding shortcuts to experts. The study showed results consistent with previous research on navigation in small worlds and focused on IR applications with richer dimensions of complexity.
机译：我们提出并调查了推荐方法来寻找分布式网络环境专家。专家发现任务，即在同行评审过程中找到“良好”的医学信息审阅者，用于基于代理的仿真环境，以评估所提出的模型的有效性，效率和可扩展性。 181个同行共同协调网络的实验表明，基于局部相关性提示的模型能够在非常短的推荐链中识别专家，并且在很大程度上超越随机散步。应用启发式来重新缠绕联系并限制使用远程邻居的使用表现出效率的大大提高，并提供了更好的网络支撑于专家的捷径。该研究表明，结果一致与之前的小世界导航研究一致，并专注于IR应用具有丰富的复杂性的尺寸。

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