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Total Results: 1,710 records

Showing results for "learned".

381. Schleyer, Titus | Digital Healthcare Research
     digital.ahrq.gov/principal-investigator/schleyer-titus
     January 01, 2023 - Schleyer, Titus The Indiana Learning Health System Initiative: Early experience developing a collaborative, regional learning health system. Citation Schleyer T, Williams L, Gottlieb J, Weaver C, Saysana M, Azar J, Sadowski J, Frederick C, Hui S, Kara A, Ruppert L, Zappone S,…

382. Data for Individual Health | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/data-individual-health
     January 01, 2023 - Data for Individual Health Project Final Report ( PDF , 8.01 MB) × Disclaimer Disclaimer details Close Project Description Publications Project Details - Completed Contract Number 14-721F-14 …

383. Fiks, Alexander | Digital Healthcare Research
     digital.ahrq.gov/principal-investigator/fiks-alexander
     January 01, 2023 - Fiks, Alexander National Center for Pediatric Practice Based Research and Learning - Final Report Citation Fiks A. National Center for Pediatric Practice Based Research and Learning - Final Report. (Prepared by the American Academy of Pediatrics under Grant No. P30 HS021645). …

384. Improving the Delivery of Health Services at the Health Systems Level | Digital Healthcare Research
     digital.ahrq.gov/2020-year-review/research-summary/improving-delivery-health-services-health-systems-level
     January 01, 2020 - Improving the Delivery of Health Services at the Health Systems Level AHRQ-funded research aims to improve the delivery of health services at the health systems or organizational level; this investment was $41.8 million over the duration of projects that were ongoing in 2020. The use…

385. Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/artificial-intelligence-based-health-it-tools-optimize-critical-care/citation/unsupervised
     January 01, 2023 - Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction. Citation Keats K, Deng S, Chen X, Zhang T, Devlin JW, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, Sikora A. Unsupervised machine learning analysis to identify patterns of ICU medication …

386. Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/anesthesiology-control-tower-feedback-alerts-supplement-treatment-actfast/citation/use
     January 01, 2023 - Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. Citation Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of machine learning to develop and evaluate models using preopera…

387. Applying machine learning in distributed data networks for pharmacoepidemiologic and pharmacovigilance studies: Opportunities, challenges, and considerations. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/improving-missing-data-analysis-distributed-research-networks/citation/applying
     January 01, 2023 - Applying machine learning in distributed data networks for pharmacoepidemiologic and pharmacovigilance studies: Opportunities, challenges, and considerations. Citation Wong J, Prieto-Alhambra D, Rijnbeek PR, Desai RJ, Reps JM, Toh S. Applying machine learning in distributed data networks for pharmacoe…

388. Developing a Passive Digital Marker for the Prediction of Childhood Asthma Treatment Response | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/developing-passive-digital-marker-prediction-childhood-asthma-treatment
     July 31, 2025 - Developing a Passive Digital Marker for the Prediction of Childhood Asthma Treatment Response Project Description Publications Applying novel machine learning methodologies in real time to readily available risk and prognostic data in electronic health records could contr…

389. Early identification of epilepsy surgery candidates: A multicenter, machine learning study. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/optimal-methods-notifying-clinicians-about-epilepsy-surgery-patients/citation/early
     January 01, 2023 - Early identification of epilepsy surgery candidates: A multicenter, machine learning study. Citation Wissel BD, Greiner HM, Glauser TA, Pestian JP, Kemme AJ, Santel D, Ficker DM, Mangano FT, Szczesniak RD, Dexheimer JW. Early identification of epilepsy surgery candidates: A multicenter, machine learni…

390. Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/using-electronic-health-record-identify-children-likely-suffer-last-minute/citation/mining
     January 01, 2023 - Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery. Citation Liu L, Ni Y, Zhang N, Nick Pratap J. Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery. Int …

391. Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/artificial-intelligence-based-health-it-tools-optimize-critical-care/citation/machine
     January 01, 2023 - Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU. Citation Sikora A, Zhang T, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, Chen X, Buckley MS, Rowe S, Devlin JW. Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU…

392. Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/improving-diabetes-and-depression-self-management-adaptive-mobile-messaging/citation/adaptive
     January 01, 2023 - Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions. Citation Figueroa CA, Aguilera A, Chakraborty B, Modiri A, Aggarwal J, Deliu N, Sarkar U, Jay Williams J, Lyles CR. Adaptive learning algorithms to optimize mobile applications for beh…

393. Administrator | Digital Healthcare Research
     digital.ahrq.gov/population/administrator
     January 01, 2024 - Administrator Facilitators and barriers to integrating patient-generated blood pressure data into primary care EHR workflows. Citation Canfield SM, Koopman RJ. Facilitators and barriers to integrating patient-generated blood pressure data into primary care EHR workflows. Appl …

394. American Academy of Pediatrics | Digital Healthcare Research
     digital.ahrq.gov/organization/american-academy-pediatrics
     January 01, 2023 - American Academy of Pediatrics National Center for Pediatric Practice Based Research and Learning - 2012 Principal Investigator Wasserman, Richard Project Name National Center for Pediatric Practice-Based Research and Learning National Cen…

395. Devine, Emily B. | Digital Healthcare Research
     digital.ahrq.gov/principal-investigator/devine-emily-b
     January 01, 2023 - Devine, Emily B. Customizing Value-Based Methods to Prioritize Implementation of Pharmacogenomic Clinical Decision Support for Learning Health Systems - Final Report Citation Devine E. Customizing Value-Based Methods to Prioritize Implementation of Pharmacogenomic Clinical Dec…

396. A National Web Conference on Evaluation of Personal Health Record (PHR) Systems and their Impact on Chronic Disease (pdf file)
     digital.ahrq.gov/sites/default/files/docs/phr-impact-chronic-disease-slides-012512.pdf
     January 25, 2012 - Implementing PHRs for Patients with Chronic Disease: Lessons Learned Peggy J. Wagner, Ph.D. … Moderator and Presenters�Disclosures Implementing PHRs for Patients with Chronic Disease: Lessons Learned

397. Leveraging Health System Telehealth and Informatics Infrastructure to Create a Continuum of Services for COVID-19 Screening, Testing, and Treatment: A Learning Health System Approach - Final Report | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/leveraging-health-system-telehealth-and-informatics-infrastructure-create/final-report
     January 01, 2023 - Leveraging Health System Telehealth and Informatics Infrastructure to Create a Continuum of Services for COVID-19 Screening, Testing, and Treatment: A Learning Health System Approach - Final Report Citation Simpson K., Harvey J. Leveraging Health System Telehealth and Informatics Infrastructure to Cre…

398. Improving the Delivery of Health Services at the Health Systems Level - Emerging Research | Digital Healthcare Research
     digital.ahrq.gov/2020-year-review/research-summary/improving-delivery-health-services-health-systems-level-emerging-research
     January 01, 2020 - across different healthcare systems and technologies (e.g., different EHRs) and disseminate lessons learned

399. Electronic Data Methods (EDM) Forum for Comparative Effectiveness Research: Second Phase - Final Report (pdf file)
     digital.ahrq.gov/sites/default/files/docs/citation/u18hs022789-edmunds-final-report-2017.pdf
     January 01, 2017 - month to share opportunities and shared challenges to using electronic health data, as well as lessons learned … CIELO); grew a community of diverse stakeholders committed to sharing the best practices and lessons learned

400. Machine learning techniques for prediction of early childhood obesity. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/computer-automated-developmental-surveillance-and-screening/citation/machine
     January 01, 2023 - Machine learning techniques for prediction of early childhood obesity. Citation Dugan TM, Mukhopadhyay S, Carroll A, et al. Machine learning techniques for prediction of early childhood obesity. Appl Clin Inform 2015 Aug 12;6(3):506-20. PMID: 26448795 Link https://www.ncbi.nlm.nih.gov/pubmed/…