ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2017
Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020Article
- A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization.Methods in molecular biology (Clifton, N.J.) · 2019Article
- Temporal Poisson Square Root Graphical Models.Proceedings of machine learning research · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Several prominent public health hazards [29] that occurred at the beginning of this century due to adverse drug events (ADEs) have raised international awareness of governments and industries about pharmacovigilance (PhV) [6,7], the science and activities to monitor and prevent adverse events caused by pharmaceutical products after they are introduced to the market. A major data source for PhV is large-scale longitudinal observational databases (LODs) [6] such as electronic health records (EHRs) and medical insurance claim databases. Inspired by the Self-Controlled Case Series (SCCS) model [27], arguably the leading method for ADE discovery from LODs, we propose baseline regularization, a regularized generalized linear model that leverages the diverse health profiles available in LODs across different
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.