Adverse Drug Effect Detection

Large collections of electronic patient records provide abundant but under-explored information on the real-world use of medicines. Although they are maintained for patient administration, they provide a broad range of clinical information for data analysis. One growing interest is drug safety signal detection from these longitudinal observational data. In this paper, we proposed two novel algorithms-a likelihood ratio model and a Bayesian network model-for adverse drug effect discovery. Although the performance of these two algorithms is comparable to the state-of-the-art algorithm, Bayesian confidence propagation neural network, the combination of three works better due to their diversity in solutions. Since the actual adverse drug effects on a given dataset cannot be absolutely determined, we make use of the simulated observational medical outcomes partnership (OMOP) dataset constructed with the predefined adverse drug effects to evaluate our methods. Experimental results show the usefulness of the proposed pattern discovery method on the simulated OMOP dataset by improving the standard baseline algorithm-chi-square-by 23.83%.

[1]  Ted Dunning,et al.  Accurate Methods for the Statistics of Surprise and Coincidence , 1993, CL.

[2]  William DuMouchel,et al.  Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System , 1999 .

[3]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.

[4]  S. R. Fine,et al.  ADVERSE DRUG REACTIONS , 2009, BMJ : British Medical Journal.

[5]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[6]  P Ryan,et al.  Novel Data‐Mining Methodologies for Adverse Drug Event Discovery and Analysis , 2012, Clinical pharmacology and therapeutics.

[7]  G. Niklas Norén,et al.  Temporal pattern discovery for trends and transient effects: its application to patient records , 2008, KDD.

[8]  P. Corey,et al.  Incidence of Adverse Drug Reactions in Hospitalized Patients , 2012 .

[9]  Munir Pirmohamed,et al.  Fortnightly review: Adverse drug reactions , 1998 .

[10]  A. Bate,et al.  A Bayesian neural network method for adverse drug reaction signal generation , 1998, European Journal of Clinical Pharmacology.

[11]  S. Evans,et al.  Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports , 2001, Pharmacoepidemiology and drug safety.

[12]  Martijn J Schuemie,et al.  Methods for drug safety signal detection in longitudinal observational databases: LGPS and LEOPARD , 2011, Pharmacoepidemiology and drug safety.