2017 · 12 citations · 17 references
ComputingComputer ScienceHealth InformaticsClinical InformaticsMachine LearningPharmacovigilanceAdverse Drug Event DetectionAdverse Drug Event ReportingAdverse Drug Reaction MonitoringAdverse Event AnalysisAdverse Event ReportingClinical Natural Language ProcessingAdverse Event DetectionAutomated Adverse Event ClassificationMachine Learning ClassificationMachine Learning For HealthcareManual LoggingSerious Adverse Event ClassificationSerious Adverse Event DetectionSerious Adverse Events
Adverse Events (AEs) are a significant concern in healthcare, since it is among the leading causes of morbidity and mortality[12]. According to the Food and Drug Administration (FDA), between 2006 and 2014, there was a 232% increase in AE cases reported to have caused mortality[13]. In fact, the volume of all AE cases reported to the FDA has increased by almost five fold since 1997[13]. Pharmaceutical companies are struggling to handle the increased case volume due to manual logging of individual cases. This is not a sustainable solution as we see the volume of AE case logs increase exponentially [12,13]. In this paper, we discuss our work and findings for implementing a pharmacovigilance automation solution. This solution explores machine learning techniques in being able to identify serious vs non-serious adverse event narrative logs. While developing our methodology, we explored both traditional machine learning and deep learning techniques. Our final model achieved a mean F1-Score of 95% and an MCC score of 0.80 on the AE case narratives. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
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Leo Breiman · Machine Learning · 2001 · 126.2K citations · Full text
Sepp Hochreiter, Jürgen Schmidhuber · Neural Computation · 1997 · 98.1K citations
Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams · Nature · 1986 · 30.9K citations