Department of Internal Medicine, Niigata University Faculty of Medicine, Niigata, Japan
Copyright © 2023 Korean Diabetes Association
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Study | Country | No. of participants | Type of drugs | Algorithms | Validation methods | Results |
---|---|---|---|---|---|---|
Liu et al. (2013) [52] | China | 82 | Metformin, insulin secretagogues or α-glucosidase inhibitors, thiazolidinediones, DPP-4I, insulin | K-Nearest Neighbor | ND | 80.2% match with real prescriptions |
Wright et al. (2015) [53] | USA | 161,497 | α-Glucosidase inhibitor, amylin analog, biguanide, bromocriptine, DPP-4I, GLP-1RA, insulin, meglitinide, PPARγ agonist, sulfonylurea | CSPADE algorithms | 10-Fold cross validation | 89.1%–90.5% at the drug class level |
Mei et al. (2017) [54] | China | 21,796 | Biguanide, sulfonylurea, glinides, thiazolidinediones, a-glucosidase inhibitors, DPP-4I, insulin | Recurrent Neural Network | Trained on 80% of the cohort and validated on 10% | AUC 0.91–0.94 |
Tarumi et al. (2021) [55] | USA | 27,904 | Metformin, sulfonylurea, DPP-4I, SGLT2I, thiazolidinediones, GLP-1RA, long-acting insulin | Gradient Boosting Tree, Treatment Pathway Graphbased Estimation, Random Forest | 5-Fold cross validation | ND |
Fujihara et al. (2021) [56] | Japan | 4,567 | Insulin | Neural Network | 5-Fold cross | AUC 0.67–0.74 |
Singla et al. (2022) [57] | India | 4,974 | Metformin, sulfonylurea, DPP-4I, SGLT2I, thiazolidinediones, pre-mix insulin, basal insulin | Random forest algorithms | ND | Accuracy 85%–99.4% |
DPP-4I, dipeptidyl peptidase-4 inhibitor; ND, not described; GLP1-RA, glucagon-like peptide 1 receptor agonist; PPARγ, peroxisome proliferator-activated receptor γ; CSPADE, sequential pattern discovery using equivalence classes; AUC, area under the curve; SGLT2I, sodium-glucose co-transporter-2 inhibitor.