Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes

Author:

Ridgway Jessica P.ORCID,Lee Alice,Devlin Samantha,Kerman Jared,Mayampurath Anoop

Publisher

Springer Science and Business Media LLC

Subject

Infectious Diseases,Virology

Reference59 articles.

1. The Office of the National Coordinator for Health Information Technology. Health IT Dashboard. https://dashboard.healthit.gov/apps/health-information-technology-data-summaries.php?state=National&cat9=all+data&cat1=ehr+adoption#summary-data. Accessed November 29, 2020. .

2. Ahlstrom MG, Ronit A, Omland LH, Vedel S, Obel N. Algorithmic prediction of HIV status using nation-wide electronic registry data. EClinicalMedicine. 2019;17:100203. https://doi.org/10.1016/j.eclinm.2019.10.016.

3. Feller DJ, Zucker J, Yin MT, Gordon P, Elhadad N. Using clinical notes and natural language processing for automated HIV risk assessment. J Acquir Immune Defic Syndr. 2018;77(2):160–6. https://doi.org/10.1097/QAI.0000000000001580.

4. Force UPST. Screening for HIV infection: US preventive services task force recommendation statement. JAMA. 2019;321(23):2326–36. https://doi.org/10.1001/jama.2019.6587.

5. Branson BM, Handsfield HH, Lampe MA, Janssen RS, Taylor AW, Lyss SB, et al. Revised recommendations for HIV testing of adults, adolescents, and pregnant women in health-care settings. MMWR Recomm Rep. 2006;55(RR-14):1–17 quiz CE1-4.

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