Journal of biomedical informatics · 2025
A joint learning framework for analyzing data from national geriatric centralized networks: A new toolbox deciphering real-world complexity.
СтатьяПоддержка NIH: внешняя программаПоддержка службы здравоохранения СШАПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Shen Biyi, Zhang Yilin, Travison Thomas G, Shardell Michelle, McCoy Rozalina G, Saegusa Takumi, Falvey Jason, Chen Chixiang
Аннотация
OBJECTIVE: We propose JLNet, along with a companion R software package, as a systematic joint learning framework for analyzing data from national geriatric centralized networks, such as Medicare Claims. JLNet addresses key challenges in real-world, large-scale healthcare datasets, including hospital-level clustering and heterogeneity, patient-level variability from high-dimensional covariates, and losses to follow-up, while promoting easy implementation to ultimately support decision-making.
METHODS: JLNet proceeds in three steps: (1) fit a dynamic propensity score model to handle patient loss to follow-up; (2) fit a projection-based regularized regression to identify predictive patient-level features while adjusting for hospital-level confounding; and (3) perform hospital-level clustering using transformed residuals, enabling downstream analyses without sharing raw data. We applied JLNet to Medicare claims data to study post-fracture recovery among older adults with Alzheimer's disease and related dementias (ADRD) following a hip fracture (2010-2018), and evaluated its performance via numerical experiments.
RESULTS: JLNet identified clinically meaningful patient-level variables (e.g., age, weight loss, peripheral vascular disease, etc.) and distinct hospital clusters associated with variation in post-discharge recovery, measured by days at home, among patients with ADRD. Numerical experiments showed that JLNet outperformed existing approaches in variable selection and hospital clustering in the setting involving high-dimensional covariates and unmeasured hospital-level confounding.
DISCUSSION AND CONCLUSION: JLNet is a scalable, interpretable framework for analyzing centralized health data. It enhances identification of high-risk subcohorts and hospital clusters, supporting more precise resource allocation and personalized care strategies for high-risk older adults. Findings also inform the design of tailored interventions in real-world settings.
METHODS: JLNet proceeds in three steps: (1) fit a dynamic propensity score model to handle patient loss to follow-up; (2) fit a projection-based regularized regression to identify predictive patient-level features while adjusting for hospital-level confounding; and (3) perform hospital-level clustering using transformed residuals, enabling downstream analyses without sharing raw data. We applied JLNet to Medicare claims data to study post-fracture recovery among older adults with Alzheimer's disease and related dementias (ADRD) following a hip fracture (2010-2018), and evaluated its performance via numerical experiments.
RESULTS: JLNet identified clinically meaningful patient-level variables (e.g., age, weight loss, peripheral vascular disease, etc.) and distinct hospital clusters associated with variation in post-discharge recovery, measured by days at home, among patients with ADRD. Numerical experiments showed that JLNet outperformed existing approaches in variable selection and hospital clustering in the setting involving high-dimensional covariates and unmeasured hospital-level confounding.
DISCUSSION AND CONCLUSION: JLNet is a scalable, interpretable framework for analyzing centralized health data. It enhances identification of high-risk subcohorts and hospital clusters, supporting more precise resource allocation and personalized care strategies for high-risk older adults. Findings also inform the design of tailored interventions in real-world settings.