一种新的罕见处方副作用发现的半监督算法
A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery
摘要：
为了改善每个患者的医疗状况，经常给患者开处方药，但大多数处方药的不幸后果是发生不希望的副作用。千分之一以上的患者发生的副作用可能通过当前的药物监测方法有效地发出信号，然而，这些相同的方法可能需要几十年才能产生罕见副作用的信号，从而在未发现罕见副作用的情况下，在处方药物的患者中存在医疗发病率或死亡率的风险。在本文中，我们提出了一种新的计算荟萃分析框架信号罕见的副作用，整合现有的方法，从网络上的知识，度量学习和半监督聚类。这种新的框架能够表明许多已知的罕见和严重的副作用，用于选择所研究的药物，例如，当处方环丙沙星或左氧氟沙星时肌腱断裂，与萘普生相关的肾衰竭和与利莫那班相关的抑郁症。此外，对于研究的大多数药物来说，它以比现有方法更严格的信号阈值产生罕见副作用的信号，并显示出有可能成为上市后监测的基本部分，以检测罕见副作用。
英文摘要：
Drugs are frequently prescribed to patients with the aim of improving each patient's medical state, but an unfortunate consequence of most prescription drugs is the occurrence of undesirable side effects. Side effects that occur in more than one in a thousand patients are likely to be signalled efficiently by current drug surveillance methods, however, these same methods may take decades before generating signals for rarer side effects, risking medical morbidity or mortality in patients prescribed the drug while the rare side effect is undiscovered. In this paper we propose a novel computational meta-analysis framework for signalling rare side effects that integrates existing methods, knowledge from the web, metric learning and semi-supervised clustering. The novel framework was able to signal many known rare and serious side effects for the selection of drugs investigated, such as tendon rupture when prescribed Ciprofloxacin or Levofloxacin, renal failure with Naproxen and depression associated with Rimonabant. Furthermore, for the majority of the drug investigated it generated signals for rare side effects at a more stringent signalling threshold than existing methods and shows the potential to become a fundamental part of post marketing surveillance to detect rare side effects.
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