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NJIT computer scientist publishes new algorithm cluster to data mine health records
Date:5/14/2013

The time may be fast approaching for researchers to take better advantage of the vast amount of valuable patient information available from U.S. electronic health records. Lian Duan, an NJIT computer scientist with an expertise in data mining, has done just that with the recent publication of "Adverse Drug Effect Detection," IEEE Journal of Biomedical and Health Informatics (March, 2013).

The article spotlights a new and promising way of using a combination of commonly used existing algorithms to root out more information about adverse drug reactions within electronic health records available to the researchers. The new pattern, which when compared against the most commonly used existing sole algorithm, showed an almost 25 percent improvement in outcome. Although the idea could theoretically be applied beyond electronic health records, this paper focuses only on using them to find adverse medical reactions to a drug therapy.

"Large collections of electronic patient records have long provided abundant, but under-explored information on the real-world use of medicines. But when used properly these records can provide longitudinal observational data which is perfect for data mining," Duan said. "Although such records are maintained for patient administration, they could provide a broad range of clinical information for data analysis. A growing interest has been drug safety."

In this paper, the researchers proposed two novel algorithmsa likelihood ratio model and a Bayesian network modelfor 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, by combining three works, the researchers say one can get better, more diverse results.

Since the actual adverse drug effects on a given dataset cannot be absolutely determined, the researchers made use of a simulated observational medical outcomes
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Contact: Sheryl Weinstein
973-596-3436
New Jersey Institute of Technology
Source:Eurekalert

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