Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV.

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. R² = 0.984), ESTAR (Adj. R² = 0.963), and BERGER (Adj. R² = 0.936). Moderate performance was observed for the P3CEQ (Adj. R² = 0.753) and TSQM (Adj. R² = 0.698), reflecting variability in model accuracy across instruments. Additionally, the model demonstrated strong reliability in maintaining standardized prediction errors below 0.2 for most instruments, with probabilities of achieving this threshold being 96.43% for WHOQoL HIV Bref and 88.44% for ESTAR, while lower probabilities were observed for TSQM (44%) and WRFQ (51%).

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.