Data mining methodologies for pharmacovigilance

Abstract

Medicines are designed to cure, treat, or prevent diseases; however, there are also risks in taking any medicine -particularly short term or long term adverse drug reactions (ADRs) can cause serious harm to patients. Adverse drug events have been estimated to cause over 700,000 emergency department visits each year in the United States. Thus, for medication safety, ADR monitoring is required for each drug throughout its life cycle, including early stages of drug design, different phases of clinical trials, and postmarketing surveillance. Pharmacovigilance (PhV) is the science that concerns with the detection, assessment, understanding and prevention of ADRs. In the pre-marketing stages of a drug, PhV primarily focuses on predicting potential ADRs using preclinical characteristics of the compounds (e.g., drug targets, chemical structure) or screening data (e.g., bioassay data). In the postmarketing stage, PhV has traditionally involved in mining spontaneous reports submitted to national surveillance systems. The research focus is currently shifting toward the use of data generated from platforms outside the conventional framework such as electronic medical records (EMRs), biomedical literature, and patient-reported data in online health forums. The emerging trend of PhV is to link preclinical data from the experimental platform with human safety information observed in the postmarketing phase. This article provides a general overview of the current computational methodologies applied for PhV at different stages of drug development and concludes with future directions and challenges.

Systematic review and meta‐analysis of algorithms used to identify drug‐induced liver injury (DILI) in health record databases

“…The advent of electronic medical records (EMR) in healthcare systems provides opportunity to harness the power of data mining via rule‐based or machine learning algorithms to detect and build catalogues for DILI. Several studies have looked into detection of adverse drug events (ADE) using routinely collected data such as diagnosis codes and laboratory parameters . Nonetheless, this is particularly challenging for DILI because competing aetiologies mimicking DILI have to be ruled out via a diagnostic algorithm that incorporates careful physical examination, history taking and a series of serological and imaging tests .…”

Systems Approaches in Risk Assessment

“…These in silico findings were supported by in vitro receptor‐ and transporter‐binding studies 40 . The key concepts and recent advances of these approaches have been reviewed elsewhere 9 , 38 , 41 , 42 , 43 , 44 , 45 …”

Mining Sequential Patterns of Diseases Contracted and Medications Prescribed before the Development of Stevens-Johnson Syndrome in Taiwan

“…However, when taking any medication, some risks should be taken seriously. Severe adverse drug reactions (ADRs) can lead to patient morbidity [1]. In the U.S., ADRs have been Moreover, some studies have indicated that many Taiwanese, who are Han Chinese, have a gene called HLA-B 1502, which increases the risk of SJS induced by medications [35,36].…”