Using machine learning to identify patients at risk of acquiring HIV in an urban health system-Reference-Cited by-同舟云学术

Using machine learning to identify patients at risk of acquiring HIV in an urban health system

Published:2024-06-03 Issue: Volume: Page:
ISSN:1525-4135
Container-title:JAIDS Journal of Acquired Immune Deficiency Syndromes
language:en
Short-container-title:

Author:

Nethi Arun Kumar¹,Karam Albert George¹^ORCID,Alvarez Kristin S.²,Luque Amneris Esther³,Nijhawan Ank E.³,Adhikari Emily⁴,King Helen Lynne³

Affiliation:

1. PCCI 8435 N Stemmons Fwy, #1150, Dallas, Texas 75247, USA

2. Center of Innovation and Value at Parkland Health, Dallas, Texas, USA

3. Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine University of Texas Southwestern Medical Center, Dallas, Texas, USA

4. Department of Obstetrics & Gynecology, Division of Maternal Fetal Medicine University of Texas Southwestern Medical Center, Dallas, Texas, USA

Publisher

Ovid Technologies (Wolters Kluwer Health)

Reference17 articles.

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2. Insufficient PrEParation: an assessment of primary care prescribing habits and use of pre-exposure prophylaxis in patients at risk of HIV acquisition at a single medical centre;Babiarz;Sex Transm Infect,2023

3. Health care provider barriers to HIV pre-exposure prophylaxis in the United States: a systematic review;Pleuhs;AIDS Patient Care STDS,2020

4. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost;Hou;J Transl Med,2020

5. Parkland trauma index of mortality: real-time predictive model for trauma patients;Starr;J Orthop Trauma,2022