A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection
Year: 2024
Type: article
Abstract: The long-term complications of COVID-19, known as the post-acute sequelae of SARS-CoV-2 infection (PASC), significantly burden healthcare resources. Quantifying the demand for post-acute healthcare is essential for understanding patients' needs and optimizing the allocation of valuable medical resources for disease management. Driven by this need, we developed a heterogeneous latent transfer learning framework (Latent-TL) to generate critical insights for individual health systems in a distributed research network. Latent-TL enhances learning in a specific health system by borrowing information from all other health systems in the network in a data-driven fashion. By identifying subpopulations with varying healthcare needs, our Latent-TL framework can provide more effective guidance for decision-making. Applying Latent-TL to electronic health record (EHR) data from eight health systems in PEDSnet, a national learning health system in the US, revealed four distinct patient subpopulations with heterogeneous post-acute healthcare demands following COVID-19 infections, varying across subpopulations and hospitals.
Source: Patterns
Institutions: University of Pittsburgh, University of Pennsylvania, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Johns Hopkins University, Children's Hospital of Philadelphia, Leonard Davis Institute of Health Economics, Cedars-Sinai Medical Center, University of Pennsylvania Health System
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,900
Awards: Advancing Drug Repositioning for Alzheimer’s Disease using Real-world Data, G2797749627, Dynamic learning for post-vaccine event prediction using temporal information in VAERS, Methods for Estimating Disease Burden of Seasonal Influenza, G4890456917, AI-ADRD: Accelerating interventions of AD/ADRD via Machine learning methods, Surrogate Augmented Deep Predictive Learning for Retinopathy of Prematurity, Coordinating Individually Measured Phenotypes to Advance Mental Health Research, G9000027382, G9000053926, G9000115331, G9000264030, PANDA-MSD: Predictive Analytics via Networked Distributed Algorithms for Multi-System Diseases, TRiPOD: Toward Reusable Phenotypes in Observational Data for AD/ADRD - managing definitions and correcting bias
OpenAlex ID: W4403729809