ArticleIntegrated pharmacy research & practice2025
Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV.
Article in Integrated pharmacy research & practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The NAVETA obesity consensus tool: a multidimensional framework for clinical and patient-reported outcome assessment.Frontiers in endocrinology · 2025Guideline
- Digital Monitoring of Pre-Exposure Prophylaxis Users Through Electronic Patient-Reported Outcome Measures and Electronic Patient-Reported Experience Measures: Multicenter Prospective Study on Feasibility, Safety, and Predictive Modeling of Digital Engagement.Journal of medical Internet research · 2026Observational
- CRSwNP in the biologics era: evaluating patient-reported quality of life outcomes.Frontiers in allergy · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In the realm of Evidence-Based Medicine, introduced by Gordon Guyatt in the early 1990s, the integration of machine learning technologies marks a significant advancement towards more objective, evidence-driven healthcare. Evidence-Based Medicine principles focus on using the best available scientific evidence for clinical decision-making, enhancing healthcare quality and consistency by integrating this evidence with clinician expertise and patient values. Patient-Reported Outcome Measures (PROMs) and Patient-Reported Experience Measures (PREMs) have become essential in evaluating the broader impacts of treatments, especially for chronic conditions like HIV, reflecting patient health and well-being comprehensively. Purpose: The study aims to leverage Machine Learning (ML) technologies to predict health outcomes from PROMs/PREMs data, focusing on people living with HIV. Patients and Methods: Our research utilizes a ML Random Forest Regression to analyze PROMs/PREMs data collected from over 1200 people living with HIV through the NAVETA telemedicine system. Results: The findings demonstrate the potential of ML algorithms to provide precise and consistent predictions of health outcomes, indicating high reliability and effectiveness in clinical settings. Notably, our ALGOPROMIA ML model achieved the highest predictive accuracy for questionnaires such as MOS30 VIH (Adj. Conclusion: The results from our machine learning algorithms are promising for predicting PROMs and PREMs in AIDS settings. This work highlights how integrating ML technologies can enhance clinical pharmaceutical decision-making and support personalized treatment strategies within a multidisciplinary integration framework. Furthermore, leveraging platforms like NAVETA for deploying these models presents a scalable approach to implementation, fostering patient-centered, value-based care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.