ibm watson truven health marketscan commercial claims and encounters (ccae) database (Truven Health)
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Truven Health
ibm watson truven health marketscan commercial claims and encounters (ccae) database
Ibm Watson Truven Health Marketscan Commercial Claims And Encounters (Ccae) Database, supplied by Truven Health, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/marketscan+commercial+claims+and+encounters+(ccae)+database/truven+ibm+watson+health+marketscan+medicaid+database/pm40586645-62-9-3
Average 90 stars, based on 1 article reviews
Ibm Watson Truven Health Marketscan Commercial Claims And Encounters (Ccae) Database, supplied by Truven Health, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/marketscan+commercial+claims+and+encounters+(ccae)+database/truven+ibm+watson+health+marketscan+medicaid+database/pm40586645-62-9-3
Average 90 stars, based on 1 article reviews
ibm watson truven health marketscan commercial claims and encounters (ccae) database - by Bioz Stars,
2026-09
90/100 stars
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Plasmid Purification:Article Title: Post-Marketing Safety Surveillance for the Adjuvanted Recombinant Zoster Vaccine: Methodology Article Snippet: US claims databases such as the Article Title: Short term physician visits and medication prescriptions for allergic disease associated with seasonal tree, grass, and weed pollen exposure across the United States Article Snippet: For the years 2008–2015, we obtained health data from the Article Title: Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery. Article Snippet: We undertook the following steps to develop and validate our PLP model.18 First, we first defined our target population in whom we wish to predict the outcome (patients undergoing metabolic surgery who had a baseline diagnosis of T2D and antihyperglycemic medication treatment); second, we defined our outcome (complete cessation of antihyperglycemic medication from 365 to 730 days after metabolic surgery); third, we selected a database in which we could obtain data on a large sample of the target population (Truven MarketScan Commercial Claims and Encounters [CCAE] Database); fourth, among the target population extracted from the database, we subdivided the sample into a training set (comprising 75% of the sample) in which we initially developed the PLP model and a separate test set (comprising the remaining 25% of the sample) in which we assessed the internal performance (discrimination/calibration) of the model; fifth, we selected a separate database in which we could obtain data on another large sample of the target population (Optum Clinformatics Database [Optum]) and applied the trained PLP model to assess the external validity of the model. Biomarker Discovery:Article Title: Post-Marketing Safety Surveillance for the Adjuvanted Recombinant Zoster Vaccine: Methodology Article Snippet: US claims databases such as the Article Title: Short term physician visits and medication prescriptions for allergic disease associated with seasonal tree, grass, and weed pollen exposure across the United States Article Snippet: For the years 2008–2015, we obtained health data from the Article Title: Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery. Article Snippet: We undertook the following steps to develop and validate our PLP model.18 First, we first defined our target population in whom we wish to predict the outcome (patients undergoing metabolic surgery who had a baseline diagnosis of T2D and antihyperglycemic medication treatment); second, we defined our outcome (complete cessation of antihyperglycemic medication from 365 to 730 days after metabolic surgery); third, we selected a database in which we could obtain data on a large sample of the target population (Truven MarketScan Commercial Claims and Encounters [CCAE] Database); fourth, among the target population extracted from the database, we subdivided the sample into a training set (comprising 75% of the sample) in which we initially developed the PLP model and a separate test set (comprising the remaining 25% of the sample) in which we assessed the internal performance (discrimination/calibration) of the model; fifth, we selected a separate database in which we could obtain data on another large sample of the target population (Optum Clinformatics Database [Optum]) and applied the trained PLP model to assess the external validity of the model. |