# A Longitudinal Attribute-Conditioned Neural Network for Modeling Health-State Transition Probabilities in Temporally Irregular Data: The LANTERN Framework Source: [A Longitudinal Attribute-Conditioned Neural Network for Modeling Health-State Transition Probabilities in Temporally Irregular Data: The LANTERN Framework]() Collection index: [llms.txt]() Data updated: 2026-06-16T05:00:05.515Z Authors: Bright Kwaku Manu, Beckett Sterner, Petar Jevtic Published: 2026-06-11T20:15:14.000Z ## Abstract Accurate estimation of long-term care transition probabilities is central to disability insurance pricing, reserving, and solvency assessment. Classical actuarial multi-state models commonly rely on Markov, semi-Markov, or proportional-hazard specifications, which provide a direct connection to cohort projection but may be restrictive for irregular longitudinal health data with nonlinear aging patterns and heterogeneous covariate histories. This paper develops a well-calibrated estimator of multi-state transition probabilities for irregular longitudinal health data. The model learns from individual health history, incorporates the time elapsed between observations, and conditions transition probabilities on demographic and socioeconomic attributes. It produces a valid probability distribution over the next observed health state, with four possible states: healthy, mild disability, severe disability, and death. Individual probabilities are aggregated by age group and origin state to form transition matrices compatible with actuarial cohort projection. Using longitudinal data from the Health and Retirement Study, we compare the proposed estimator with logistic regression, gradient-boosted trees, a recurrent neural network, and a last-state persistence benchmark. The evaluation considers probabilistic accuracy, endpoint discrimination and calibration for severe disability and death, risk concentration, and transition matrix error after aggregation. The proposed estimator improves severe disability discrimination relative to logistic regression and gradient-boosted tree benchmarks, maintains strong calibration, and yields the lowest transition matrix error among the evaluated models in the held-out test analysis. Results show that a structured machine learning estimator can support long-term care transition modeling when judged by calibration and projection fidelity, beyond discrimination. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Actuarial projection on Held-out (test)](): Actuarial projection; held-out (test); MAE: 0.0198. - [Endpoint risk prediction on Individual-level data Severe disability endpoint (test)](): Endpoint risk prediction; Individual-level data Severe disability endpoint (test); AUROC: 0.911. - [Endpoint risk prediction on Individual-level data Death endpoint (test)](): Endpoint risk prediction; Individual-level data Death endpoint (test); AUROC: 0.822. - [Death Risk Stratification on HRS (test)](): Death Risk Stratification; HRS (test); Lift@10%: 4.8555. - [Severe Disability Risk Stratification on HRS (test)](): Severe Disability Risk Stratification; HRS (test); Lift@10%: 5.9508. ## Original sources and implementation [Original paper]() [PDF]()