Results

Total Results: 1,710 records

Showing results for "learned".

341. A Machine Learning Health System to Integrate Care for Substance Misuse and HIV Treatment and Prevention Among Hospitalized Patients - Final Report | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/machine-learning-health-system-integrate-care-substance-misuse-and-hiv-treatment-and-prevention/final-report
     January 01, 2023 - A Machine Learning Health System to Integrate Care for Substance Misuse and HIV Treatment and Prevention Among Hospitalized Patients - Final Report Citation Held M., Thompson H. A Machine Learning Health System to Integrate Care for Substance Misuse and HIV Treatment and Prevention Among Hospitalized …

342. AHRQ National Web Conference on Applying Advanced Analytics in Clinical Care - Q&As (pdf file)
     digital.ahrq.gov/sites/default/files/docs/clinical-care-qas-10142020.pdf
     October 14, 2020 - AHRQ National Web Conference on Applying Advanced Analytics in Clinical Care - Q&As AHRQ National Web Conference on Applying Advanced Analytics in Clinical Care Q&A Responses …

343. Learning from primary care meaningful use exemplars. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/learning-primary-care-meaningful-use-exemplars/citation/learning-primary-care
     January 01, 2023 - Learning from primary care meaningful use exemplars. Citation Ornstein SM, Nemeth LS, Nietert PJ, et al. Learning from primary care meaningful use exemplars. J Am Board Fam Med 2015;28(3):360-70. PMID: 25957369. Link https://www.ncbi.nlm.nih.gov/pubmed/25957369 Principal Investigator …

344. Machine learning approaches to identify nicknames from a statewide Health Information Exchange. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/enhancing-patient-matching-support-operational-health-information-exchange/citation/machine
     January 01, 2023 - Machine learning approaches to identify nicknames from a statewide Health Information Exchange. Citation Kasthurirathne SN, Grannis SJ. Machine learning approaches to identify nicknames from a statewide Health Information Exchange. AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:639-647. PMID: 312590…

345. Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/anesthesiology-control-tower-feedback-alerts-supplement-treatment-actfast/citation/machine
     January 01, 2023 - Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study. Citation Fritz BA, Chen Y, Murray-Torres TM, et al. Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol fo…

346. Comparison of supervised machine learning and probabilistic approaches for record linkage. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/enhancing-patient-matching-support-operational-health-information-exchange/citation/comparison
     January 01, 2023 - Comparison of supervised machine learning and probabilistic approaches for record linkage. Citation McNutt, A.T., Grannis, S.J., Bo, N., Xu, H., Kasthurirathne, S. N.(2020, March). Comparison of supervised machine learning and probabilistic approaches for record linkage. AMIA Informatics summit 2020 C…

347. Using active learning to identify health Information technology related patient safety events. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/developing-evidence-based-user-centered-design-and-implementation-guidelines/citation/using
     January 01, 2023 - Using active learning to identify health Information technology related patient safety events. Citation Fong A, Howe JL, Adams KT, et al. Using active learning to identify health Information technology related patient safety events. Appl Clin Inform 2017 Jan 18;8(1):35-46. PMID: 28097287. Link …

348. Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/artificial-intelligence-based-health-it-tools-optimize-critical-care/citation/learning
     January 01, 2023 - Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data. Citation Sikora A, Zhao B, Kong Y, Murray B, Shen Y. Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data. medRxiv [Preprint]. 202…

349. Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/exploring-clinically-relevant-image-retrieval-diabetic-retinopathy-diagnosis/citation/classification
     January 01, 2023 - Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features. Citation Venkatesan R, Chandakkar P, Li B, et al. Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram featur…

350. Research Centers in Primary Care Practice-Based Research and Learning | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/research-centers-primary-care-practice-based-research-and-learning
     January 01, 2023 - Research Centers in Primary Care Practice-Based Research and Learning Project Final Report ( PDF , 190.53 KB) Disclaimer Disclaimer The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent th…

351. A machine learning approach to reclassifying miscellaneous patient safety event reports. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/developing-evidence-based-user-centered-design-and-implementation-guidelines/citation/machine
     January 01, 2023 - A machine learning approach to reclassifying miscellaneous patient safety event reports. Citation Fong A, Behzad S, Pruitt Z, Ratwani RM. A machine learning approach to reclassifying miscellaneous patient safety event reports. J Patient Saf. 2021 Dec 1;17(8):e829-e833. doi: 10.1097/PTS.000000000000073…

352. National Center for Pediatric Practice Based Research and Learning - Final Report | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/national-center-pediatric-practice-based-research-and-learning/final-report
     January 01, 2023 - 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). Rockville, MD: Agency for Healthcare Rese…

353. 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 | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/leveraging-health-system-telehealth-and-informatics-infrastructure-create
     January 01, 2024 - 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 Project Final Report ( PDF , 336.02 KB) Disclaimer Disclaimer The findings and conclusions in thi…

354. Improving Diabetes and Depression Self-Management Via Adaptive Mobile Messaging | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/improving-diabetes-and-depression-self-management-adaptive-mobile-messaging
     January 01, 2024 - Improving Diabetes and Depression Self-Management Via Adaptive Mobile Messaging Project Final Report ( PDF , 544.36 KB) Disclaimer Disclaimer The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily repr…

355. A Machine Learning Algorithm to Improve the Use of Interpreters for Hospitalized Patients with Complex Care Needs | Digital Healthcare Research
     digital.ahrq.gov/program-overview/research-stories/machine-learning-algorithm-improve-use-interpreters-hospitalized
     January 01, 2023 - A Machine Learning Algorithm to Improve the Use of Interpreters for Hospitalized Patients with Complex Care Needs Theme: Supporting Health Systems in Advancing Care Delivery Subtheme: Improving Equity in Healthcare with Digital Healthcare Solutions A machine learning, predictive analytic i…

356. Learning From Primary Care EHR Exemplars About Health IT Safety - Final Report | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/learning-primary-care-ehr-exemplars-about-health-it-safety/final-report
     January 01, 2023 - Learning From Primary Care EHR Exemplars About Health IT Safety - Final Report Citation Ornstein S. Learning From Primary Care EHR Exemplars About Health IT Safety - Final Report. (Prepared by Medical University of South Carolina under Grant No. R21 HS024327). Rockville, MD: Agency for Healthcare Rese…

357. Identifying high need primary care patients using nursing knowledge and machine learning methods. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/implementing-personalized-cross-sector-transitional-care-management-promote/citation/identifying
     January 01, 2023 - Identifying high need primary care patients using nursing knowledge and machine learning methods. Citation Hewner S, Smith E, Sullivan SS. CIC 2022: Identifying high need primary care patients using nursing knowledge and machine learning methods. Appl Clin Inform. 2023 Mar 7. doi: 10.1055/a-2048-7343.…

358. A learning health system approach to integrating electronic patient-reported outcomes across the health care organization. | Digital Healthcare Research
     digital.ahrq.gov/ahrq-funded-projects/developing-design-principles-integrate-patient-reported-outcomes-pros-clinical/citation/learning
     January 01, 2023 - A learning health system approach to integrating electronic patient-reported outcomes across the health care organization. Citation Austin EJ, LeRouge C, Lee J, et al. A learning health system approach to integrating electronic patient-reported outcomes across the health care organization. Learning H…

359. Longitudinal Telephony & Multiple Disease Management System to Improve Ambulatory Care for Complex Patients - Final Report (pdf file)
     digital.ahrq.gov/sites/default/files/docs/publication/r18hs017855-friedman-final-report-2013.pdf
     January 01, 2013 - We explored the source of this problem and learned that typically it was the patients’ primary care … this module while the two individuals who participated in the summative interviews stated that they learned … One important lesson learned was that adequate recruitment and retention require intimate knowledge … More importantly, we learned that even though interaction with TLC-C might not have led to a tangible … Furthermore, we learned that as long as patients know and realize that there is something advantageous

360. Visual Learning: Displaying the Data for Hypertension Management | Digital Healthcare Research
     digital.ahrq.gov/program-overview/research-stories/visual-learning-displaying-data-hypertension-management
     January 01, 2023 - Visual Learning: Displaying the Data for Hypertension Management Theme: Optimizing Care Delivery for Clinicians Subtheme: Optimizing Data Visualization to Improve Care A clinical decision support tool helps patients and physicians use at-home measured blood pressure data to better understa…