# Investigating a domain adaptation approach for integrating different measurement instruments in a longitudinal clinical registry

arXiv: https://arxiv.org/abs/2312.00616 · HTML: https://mlsanity.com/paper/2312.00616/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [Domain Adaptation for Visual Applications: A Comprehensive Survey](https://mlsanity.com/paper/1702.05374/) (2017)
- [Domain Adaptation for Time Series Forecasting via Attention Sharing](https://mlsanity.com/paper/2102.06828/) (2021)
- [Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation](https://mlsanity.com/paper/1702.07841/) (2017)
- [Domain Adaptation for Time Series Under Feature and Label Shifts](https://mlsanity.com/paper/2302.03133/) (2023)
- [A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols](https://mlsanity.com/paper/1805.10170/) (2018)
- [Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series](https://mlsanity.com/paper/2307.10923/) (2023)
- [Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation](https://mlsanity.com/paper/2202.06159/) (2022)
- [Context-aware Domain Adaptation for Time Series Anomaly Detection](https://mlsanity.com/paper/2304.07453/) (2023)
- [Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?](https://mlsanity.com/paper/2012.00634/) (2020)
- [Describing complex disease progression using joint latent class models for multivariate longitudinal markers and clinical endpoints](https://mlsanity.com/paper/2202.05124/) (2022)
- [A statistical approach to latent dynamic modeling with differential equations](https://mlsanity.com/paper/2311.16286/) (2023)

## Built on by (most cited first, 3 of 3)

- [Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs](https://mlsanity.com/paper/2412.03271/) (2024)
- [Using latent representations to link disjoint longitudinal data for mixed-effects regression](https://mlsanity.com/paper/2510.25531/) (2025)
- [Distinguishing case-mix from context heterogeneity in prognostic regression model synthesis settings](https://mlsanity.com/paper/2608.12885/) (2026)

## Abstract (arXiv)

In a longitudinal clinical registry, different measurement instruments might have been used for assessing individuals at different time points. To combine them, we investigate deep learning techniques for obtaining a joint latent representation, to which the items of different measurement instruments are mapped. This corresponds to domain adaptation, an established concept in computer science for image data. Using the proposed approach as an example, we evaluate the potential of domain adaptation in a longitudinal cohort setting with a rather small number of time points, motivated by an application with different motor function measurement instruments in a registry of spinal muscular atrophy (SMA) patients. There, we model trajectories in the latent representation by ordinary differential equations (ODEs), where person-specific ODE parameters are inferred from baseline characteristics. The goodness of fit and complexity of the ODE solutions then allows to judge the measurement instrument mappings. We subsequently explore how alignment can be improved by incorporating corresponding penalty terms into model fitting. To systematically investigate the effect of differences between measurement instruments, we consider several scenarios based on modified SMA data, including scenarios where a mapping should be feasible in principle and scenarios where no perfect mapping is available. While misalignment increases in more complex scenarios, some structure is still recovered, even if the availability of measurement instruments depends on patient state. A reasonable mapping is feasible also in the more complex real SMA dataset. These results indicate that domain adaptation might be more generally useful in statistical modeling for longitudinal registry data.