Table 2 Definitions of variables in data extraction
Variable | Definitions |
|---|---|
Study characteristics | Â |
 Year | Year of publication |
 Authors | Last name of authors |
 Study type | Article, abstract, or systematic review |
 Journal, conference | Name of journal or conference |
Training set information | |
 Training set | Name of dataset used for training |
 Area | General medicine, detailed disease, or specific intervention |
 Source | Name of electronic databases searched for building training set |
 Time range | Time range of training set |
 Type of publication | Abstract, or full-text |
 Number of all literatures | Number of all literatures in training set |
 Number of included literatures | Number of included literatures identified by the step of screening in training set |
 Training method | Supervised, semi-supervised, or unsupervised |
Validation set information | |
 Validation set | Name of dataset used for validation |
 Area | General, disease, or intervention |
 Source | Name of electronic database searched for building validation set |
 Time range | Time range of validation set |
 Type of publication | Abstract, or full-text |
 Number of all literatures | Number of all literatures in validation set |
 Number of included literatures | Number of included literatures identified by the step of screening in validation set |
 Golden standard | Process of screening by human investigators |
AI algorithm information | |
 Model name | Name of model |
 Model type | Classification, regression, ranking, or others |
 Model performance | Including but not limited to sensitivity, specificity, precision, NPV, PPV, NLR, PLR, DOR, F-measure, accuracy, and AUC |
 Cost saving | Decreased number of screened literatures by human investigators |