Sentence examples for data mining variables from inspiring English sources

The data mining variables differed for each condition.

In the data mining, variables were selected based on their scientific relevance to the targeted biomarkers of potential harm.

For identifying LDL cholesterol ≥70 the PPV was 86% (95% CI: 83%, 89%) in the model using pre-specified variables, and increased to 91%9595% CI: 85%, 91%) when adding data mining variables (Table  2 and Additional file 1: Figure S2, Panel B).

In the model that included pre-specified variables, a predicted probability threshold of 0.55 yielded a PPV of 87%95%5% CI: 85%, 88%) for identifying high risk for CHD, and a sensitivity of 69%95%5% CI: 67%, 70%); results were similar after adding data mining variables (Table  2 and see Additional file 1: Figure S1, Panel A).

In the model using pre-specified variables, a predicted probability threshold of 0.28 yielded a PPV of 52%95%5% CI: 49%, 54%) for identifying very high risk for CHD events and a sensitivity of 63%95%5% CI: 59%, 66%); results were similar after adding data mining variables (Table  2 and see Additional file 1: Figure S1, Panel B).

In the model using pre-specified variables, a predicted probability threshold of 0.20 yielded a PPV of 31%95%5% CI: 27%, 36%) for identifying Framingham CHD risk score >20% and a sensitivity of 47%95%5% CI: 43%, 54%); results were similar after adding data mining variables (Table  2 and see Additional file 1: Figure S1, Panel C).

Fourth, for all five conditions, we used claims data for only one year prior to the REGARDS in-home visit to define pre-specified and data mining Medicare variables, instead of using all available claims.

Given the complexity of water-associated infectious disease, statistical data mining and variable selection techniques using tree-based searches through the model space (Breiman 2001) may be useful.

Table 5 Results of the robustness test for data mining bias Proxy variable Number of different variable operationalizations in the primary studiesa Hyp.

As regards data mining, patient-related variables like diagnosis, sex and birth date can be combined with data information in order to compose specific queries.

In several data mining pipelines, important variables were selected from an RFM, which were subsequently used in other analysis techniques [ 50, 71].