# Interpretable machine learning for high-dimensional trajectories of aging health

arXiv: https://arxiv.org/abs/2105.03410 · HTML: https://mlsanity.com/paper/2105.03410/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 78 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
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- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
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- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Universal Differential Equations for Scientific Machine Learning](https://mlsanity.com/paper/2001.04385/) (2020)
- [Scalable Gradients for Stochastic Differential Equations](https://mlsanity.com/paper/2001.01328/) (2020)
- [Revisiting Classifier Two-Sample Tests](https://mlsanity.com/paper/1610.06545/) (2016)
- [GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series](https://mlsanity.com/paper/1905.12374/) (2019)
- [Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data](https://mlsanity.com/paper/1612.08544/) (2016)
- [Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks](https://mlsanity.com/paper/1705.08982/) (2017)
- [Effective Ways to Build and Evaluate Individual Survival Distributions](https://mlsanity.com/paper/1811.11347/) (2018)
- [Deep Recurrent Survival Analysis](https://mlsanity.com/paper/1809.02403/) (2018)
- [Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods](https://mlsanity.com/paper/2012.04580/) (2020)
- [Deep learning for comprehensive forecasting of Alzheimer's Disease progression](https://mlsanity.com/paper/1807.03876/) (2018)
- [Bayesian inference for diffusion driven mixed-effects models](https://mlsanity.com/paper/1507.06807/) (2015)
- [A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure](https://mlsanity.com/paper/1601.04674/) (2016)
- [Inferring Multidimensional Rates of Aging from Cross-Sectional Data](https://mlsanity.com/paper/1807.04709/) (2018)
- [Disease-Atlas: Navigating Disease Trajectories with Deep Learning](https://mlsanity.com/paper/1803.10254/) (2018)
- [Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes](https://mlsanity.com/paper/2001.02585/) (2020)
- [Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins](https://mlsanity.com/paper/2012.13455/) (2020)
- [Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks](https://mlsanity.com/paper/2002.02779/) (2020)
- [Forecasting Individualized Disease Trajectories using Interpretable Deep Learning](https://mlsanity.com/paper/1810.10489/) (2018)

## Abstract (arXiv)

We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine techniques of modern machine learning with an interpretable interaction network, where health variables are coupled by explicit pair-wise interactions within a stochastic dynamical system. Our dynamic joint interpretable network (DJIN) model is scalable to large longitudinal data sets, is predictive of individual high-dimensional health trajectories and survival from baseline health states, and infers an interpretable network of directed interactions between the health variables. The network identifies plausible physiological connections between health variables as well as clusters of strongly connected health variables. We use English Longitudinal Study of Aging (ELSA) data to train our model and show that it performs better than multiple dedicated linear models for health outcomes and survival. We compare our model with flexible lower-dimensional latent-space models to explore the dimensionality required to accurately model aging health outcomes. Our DJIN model can be used to generate synthetic individuals that age realistically, to impute missing data, and to simulate future aging outcomes given arbitrary initial health states.