Preliminary Results from a Peer-Led, Social Network Intervention, Augmented by Artificial Intelligence to Prevent HIV among Youth Experiencing Homelessness

• URL: http://arxiv.org/abs/2007.07747v1
• Date: Sat, 11 Jul 2020 02:17:53 GMT
• Title: Preliminary Results from a Peer-Led, Social Network Intervention, Augmented by Artificial Intelligence to Prevent HIV among Youth Experiencing Homelessness
• Authors: Eric Rice, Laura Onasch-Vera, Graham T. DiGuiseppi, Bryan Wilder, Robin Petering, Chyna Hill, Amulya Yadav, Milind Tambe
• Abstract summary: Each year, there are nearly 4 million youth experiencing homelessness in the United States with HIV prevalence ranging from 3 to 11.5%. PCA models for HIV prevention have been used successfully in many populations, but there have been notable failures. We tested a new PCA intervention for YEH, with three arms: (1) an arm using an artificial intelligence (AI) planning algorithm to select PCA, (2) a popularity arm--operationalized as highest degree centrality (DC), and (3) an observation only comparison group (OBS) Both the AI and DC arms showed improvements over time. AI-based PCA selection led to better
• Score: 47.21347530335741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Each year, there are nearly 4 million youth experiencing homelessness (YEH) in the United States with HIV prevalence ranging from 3 to 11.5%. Peer change agent (PCA) models for HIV prevention have been used successfully in many populations, but there have been notable failures. In recent years, network interventionists have suggested that these failures could be attributed to PCA selection procedures. The change agents themselves who are selected to do the PCA work can often be as important as the messages they convey. To address this concern, we tested a new PCA intervention for YEH, with three arms: (1) an arm using an artificial intelligence (AI) planning algorithm to select PCA, (2) a popularity arm--the standard PCA approach--operationalized as highest degree centrality (DC), and (3) an observation only comparison group (OBS). PCA models that promote HIV testing, HIV knowledge, and condom use are efficacious for YEH. Both the AI and DC arms showed improvements over time. AI-based PCA selection led to better outcomes and increased the speed of intervention effects. Specifically, the changes in behavior observed in the AI arm occurred by 1 month, but not until 3 months in the DC arm. Given the transient nature of YEH and the high risk for HIV infection, more rapid intervention effects are desirable.

Related papers

• Using agent-based models and EXplainable Artificial Intelligence (XAI) to simulate social behaviors and policy intervention scenarios: A case study of private well users in Ireland [0.0]
  Around 50 percent of Irelands rural population relies on unregulated private wells vulnerable to agricultural runoff and untreated wastewater. High national rates of Shiga toxin-producing Escherichia coli (STEC) and other waterborne illnesses have been linked to well water exposure. This study employs Agent-Based Modeling (ABM) to simulate policy interventions based on national survey data.
  arXiv  Detail & Related papers  (2025-02-08T23:21:50Z)
• Leveraging Self-Training and Variational Autoencoder for Agitation Detection in People with Dementia Using Wearable Sensors [1.4633432285242916]
  Agitation and aggression (AA) are some of the symptoms of people with severe dementia (PwD) in long-term care or hospitals. AA not only causes discomfort but also puts the patients or others at potential risk. Existing monitoring solutions utilizing different wearable sensors integrated with Artificial Intelligence (AI) offer a way to detect AA early enough for timely and adequate medical intervention. This study presents a novel comprehensive approach to detect AA in PwD using physiological data from the Empatica E4 wristbands.
  arXiv  Detail & Related papers  (2024-12-26T15:34:25Z)
• A Novel Multimodal System to Predict Agitation in People with Dementia Within Clinical Settings: A Proof of Concept [1.3810130040348885]
  Agitation and aggression (AA) in people living with dementia (PwD) contribute to distress and increased healthcare demands. We present a 5-year study system that integrates a multimodal approach, utilizing the EmbracePlus wristband and a video detection system. The system collects and processes raw and digital biomarkers from the EmbracePlus wristband to accurately predict AA.
  arXiv  Detail & Related papers  (2024-10-26T02:59:34Z)
• How Performance Pressure Influences AI-Assisted Decision Making [57.53469908423318]
  We show how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior. Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Utilizing AI and Social Media Analytics to Discover Adverse Side Effects of GLP-1 Receptor Agonists [0.0]
  Adverse side effects (ASEs) of drugs, revealed after FDA approval, pose a threat to patient safety. We developed a digital methodology capable of analyzing massive public data from social media. We uncovered 21 potentials associated with the glucagon-like peptide 1 receptor agonists (GLP-1 RA) market.
  arXiv  Detail & Related papers  (2024-04-01T09:48:14Z)
• A Multi-Agent Reinforcement Learning Framework for Evaluating the U.S. Ending the HIV Epidemic Plan [2.498439320062193]
  Ending the HIV Epidemic initiative aims to reduce new infections by 90% by 2030. Existing HIV decision analytic models either evaluate specific cities or the overall national population, thus overlooking jurisdictional interactions or differences. We propose a multi-agent reinforcement learning (MARL) model, that enables jurisdiction-specific decision analyses but in an environment with cross-jurisdictional epidemiological interactions.
  arXiv  Detail & Related papers  (2023-11-01T21:19:35Z)
• Zero-shot causal learning [64.9368337542558]
  CaML is a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task. We show that CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training.
  arXiv  Detail & Related papers  (2023-01-28T20:14:11Z)
• Adherence Forecasting for Guided Internet-Delivered Cognitive Behavioral Therapy: A Minimally Data-Sensitive Approach [59.535699822923]
  Internet-delivered psychological treatments (IDPT) are seen as an effective and scalable pathway to improving the accessibility of mental healthcare. This work proposes a deep-learning approach to perform automatic adherence forecasting, while relying on minimally sensitive login/logout data. The proposed Self-Attention Network achieved over 70% average balanced accuracy, when only 1/3 of the treatment duration had elapsed.
  arXiv  Detail & Related papers  (2022-01-11T13:55:57Z)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)
• To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making [4.877174544937129]
  People supported by AI-powered decision support tools frequently overrely on the AI. Adding explanations to the AI decisions does not appear to reduce the overreliance. Our research suggests that human cognitive motivation moderates the effectiveness of explainable AI solutions.
  arXiv  Detail & Related papers  (2021-02-19T00:38:53Z)
• Computational Support for Substance Use Disorder Prevention, Detection, Treatment, and Recovery [62.9980747784214]
  Substance Use Disorders involve the misuse of alcohol, opioids, marijuana, and methamphetamine. 1 in 12 U.S. adults have or have had a substance use disorder. National Institute on Drug Abuse estimates that SUDs cost the U.S. $520 billion annually.
  arXiv  Detail & Related papers  (2020-06-23T18:30:20Z)
• A Machine Learning System for Retaining Patients in HIV Care [2.8550886780528493]
  51% of people living with HIV are non-adherent with their medications and eventually drop out of medical care. Current methods of re-linking individuals to care are reactive (after a patient has dropped-out) and hence not very effective. We describe our system to predict who is most at risk to drop-out-of-care for use by the University of Chicago HIV clinic and the Chicago Department of Public Health.
  arXiv  Detail & Related papers  (2020-06-01T01:44:38Z)
• A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions [63.669642197519934]
  We use the SEIR epidemiological model to represent the evolution of the virus COVID-19 over time in the population. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system. We prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses.
  arXiv  Detail & Related papers  (2020-05-15T17:17:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.