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Table 3 Characteristics of included studies

From: Big data analysis techniques to address polypharmacy in patients – a scoping review

Author, Year Country Aim No. of used observations Method of data analysis Outcome
Keine et al. 2019 [24] USA Evaluating a precision medicine platform to identify a multitude of polypharmacy problems in people with dementia and mild Alzheimer’s disease through the creation of personalized, multidomain treatment plans 295 patients with a family history of Alzheimer’s disease or mild cognitive decline Clinical decision support software (CDSS) with machine-learning algorithms The system was able to identify a multitude of polypharmacy problems that individuals are currently facing.
Kadra et al. 2015 [25] UK Extracting antipsychotic polypharmacy data from structured and free-text fields in electronic health records 7201 patients with serious mental illness Combination of natural language processing and a bespoke algorithm. Individual instances of antipsychotic prescribing, 2 or more antipsychotics prescribed in any 6 week window; antipsychotic co-prescribing for 6 months
Duke et al. 2010 [26] USA Creating a decision support system tailored to the evaluation of adverse reactions in patients on multiple medications 16,340 unique drug and side-effect pairs, representing 250 common medications A numeric score was assigned to reflect the strength of association between drug and effect. Based on this score, the system generates graphical adverse reaction maps for any user-selected combination of drugs. This tool demonstrated a 60% reduction in time to complete a query (61 s vs. 155 s, p < 0.0001) with no decrease in accuracy