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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