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Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The "count" indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to describe the creation, evolution, and implementation of a database of nursing-sensitive and potentially nursing-sensitive indicators, the Military Nursing Outcomes Database (MilNOD). It discusses data quality, utility, and lessons learned. Prospective data collected each shift include direct staff hours by levels (i.e., registered nurse, other licensed and unlicensed providers), staff categories (i.e., military, civilian, contract, and reservist), patient census, acuity, and admissions, discharges, and transfers. Retrospective adverse event data (falls, medication errors, and needle-stick injuries) were collected from existing records. Annual patient satisfaction, nurse work environment, and pressure ulcer and restraint prevalence surveys were conducted. The MilNOD contains shift level data from 56 units in 13 military hospitals and is used to target areas for managerial and clinical performance improvement. This methodology can be modified for use in other healthcare systems. As standard tools for evidence-based management, databases such as MilNOD allow nurse leaders to track the status of nursing and adverse events in their facilities. [ABSTRACT FROM AUTHOR]