Learning signals of adverse drug-drug interactions from the unstructured text of electronic health records.

Drug-drug interactions (DDI) account for 30% of all adverse drug reactions, which are the fourth leading cause of death in the US. Current methods for post marketing surveillance primarily use spontaneous reporting systems for learning DDI signals and validate their signals using the structured portions of Electronic Health Records (EHRs). We demonstrate a fast, annotation-based approach, which uses standard odds ratios for identifying signals of DDIs from the textual portion of EHRs directly and which, to our knowledge, is the first effort of its kind. We developed a gold standard of 1,120 DDIs spanning 14 adverse events and 1,164 drugs. Our evaluations on this gold standard using millions of clinical notes from the Stanford Hospital confirm that identifying DDI signals from clinical text is feasible (AUROC=81.5%). We conclude that the text in EHRs contain valuable information for learning DDI signals and has enormous utility in drug surveillance and clinical decision support

Chapter Evolving Roles of Spontaneous Reporting Systems to Assess and Monitor Drug Safety

This chapter aims to describe current and emerging roles of spontaneous reporting systems (SRSs) for assessing and monitoring drug safety. Moreover, it offers a perspective on the near future, which entails the so-called era of Big Data, keeping in mind both regulator and researcher viewpoints. After a panorama on key data sources and analyses of post-marketing data of adverse drug reactions, a critical appraisal of methodological issues and debated future applications of SRSs will be presented, including the exploitation and challenges in evidence integration (i.e., merging and combining heterogeneous sources of data into a unique indicator of risk) and patient’s reporting via social media. Finally, a call for a responsible use of these studies is offered, with a proposal on a set of minimum requirements to assess the quality of disproportionality analysis in terms of study conception, performing and reporting

Evolving Roles of Spontaneous Reporting Systems to Assess and Monitor Drug Safety

This chapter aims to describe current and emerging roles of spontaneous reporting systems (SRSs) for assessing and monitoring drug safety. Moreover, it offers a perspective on the near future, which entails the so-called era of Big Data, keeping in mind both regulator and researcher viewpoints. After a panorama on key data sources and analyses of post-marketing data of adverse drug reactions, a critical appraisal of methodological issues and debated future applications of SRSs will be presented, including the exploitation and challenges in evidence integration (i.e., merging and combining heterogeneous sources of data into a unique indicator of risk) and patient’s reporting via social media. Finally, a call for a responsible use of these studies is offered, with a proposal on a set of minimum requirements to assess the quality of disproportionality analysis in terms of study conception, performing and reporting

A Simulation-based Comparison of Drug-Drug Interaction Signal Detection Methods

Many studies have proposed methods to detect adverse drug reactions induced by taking two drugs together. These suspected adverse drug reactions can be discovered through post-market drug safety surveillance. Post-market drug safety surveillance relies on spontaneous reporting data including ADR reports and prescription information. Most previous studies have applied statistical models to real world data and compared the performance. In this article, we assess the performance of various detection methods by implementing simulations under various conditions. This allows us to determine which situation each of the methods is most useful for. In addition, we summarize and generalize the characteristics of each method. 많은 선행연구에서 두 가지 약물을 함께 복용함으로 인해 발생하는 약물 부작용을 탐지하는 방법을 연구하였다. 약물 부작용으로 의심되는 신호는 시판 후 의약품 안전 감시를 통하여 발견될 수 있다. 시판 후 의약품 안전 감시는 부작용 보고와 의약품 처방 정보에 대한 자발적 보고 데이터에 기반한다. 약물간 상호작용 신호 탐지방법에 대한 대부분의 선행 연구는 이러한 자발적 보고 데이터에 각 방법들을 적용하고 각 방법들 간의 성능을 비교하였다. 본 논문에서는 다양한 조건하에서 시물레이션을 수행하여 여러 방법들 간의 성능을 평가한다. 이를 통해 각 방법의 특성을 요약하고 어떤 상황에서 유용한지 살펴보고자 한다.open석

Discovering Drug-Drug Interactions Using Association Rule Mining from Electronic Health Records

In this paper, we propose utilising Electronic Health Records (EHR) to discover previously unknown drug-drug interactions (DDI) that may result in high rates of hospital readmissions. We used association rule mining and categorised drug combinations as high or low risk based on the adverse events they caused. We demonstrate that the drug combinations in the high-risk group contain significantly more drug-drug interactions than those in the low-risk group. This approach is efficient for discovering potential drug interactions that lead to negative outcomes, thus should be given priority and evaluated in clinical trials. In fact, severe drug interactions can have life-threatening consequences and result in adverse clinical outcomes. Our findings were achieved using a new association rule metric, which better accounts for the adverse drug events caused by DDI

Pattern discovery in adverse event data

Imperial Users onl