Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains

Dongkyu Cho, Xiyue Li, Samrachana Adhikari, Rumi Chunara
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:394-409, 2026.

Abstract

Continual learning updates models under distribution shift while limiting performance loss on previously seen data, but clinical use also motivates inspectable interfaces that can be compared across model versions. We propose Tree of Concepts, which uses a shallow decision tree to define a fixed vocabulary of root-to-leaf rules and sequentially updates a neural concept predictor and label head with replay. The association between each concept ID and its rule is fixed; predicted concept assignments and the concept-to-label mapping remain adaptive. Across the evaluated health-related tabular protocols, Tree of Concepts attains the highest or tied mean past- and current-slice scores among the compared methods. These comparisons are descriptive point estimates rather than evidence of statistical superiority. Leaf-assignment agreement with the frozen tree ranges from 0.81 to 0.91, indicating that a fixed vocabulary does not imply invariant instance-level explanations. The results motivate further evaluation of fixed rule vocabularies as longitudinally comparable interfaces for continually updated tabular models.

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-cho26a, title = {Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains}, author = {Cho, Dongkyu and Li, Xiyue and Adhikari, Samrachana and Chunara, Rumi}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {394--409}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/cho26a/cho26a.pdf}, url = {https://proceedings.mlr.press/v340/cho26a.html}, abstract = {Continual learning updates models under distribution shift while limiting performance loss on previously seen data, but clinical use also motivates inspectable interfaces that can be compared across model versions. We propose Tree of Concepts, which uses a shallow decision tree to define a fixed vocabulary of root-to-leaf rules and sequentially updates a neural concept predictor and label head with replay. The association between each concept ID and its rule is fixed; predicted concept assignments and the concept-to-label mapping remain adaptive. Across the evaluated health-related tabular protocols, Tree of Concepts attains the highest or tied mean past- and current-slice scores among the compared methods. These comparisons are descriptive point estimates rather than evidence of statistical superiority. Leaf-assignment agreement with the frozen tree ranges from 0.81 to 0.91, indicating that a fixed vocabulary does not imply invariant instance-level explanations. The results motivate further evaluation of fixed rule vocabularies as longitudinally comparable interfaces for continually updated tabular models.} }
Endnote
%0 Conference Paper %T Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains %A Dongkyu Cho %A Xiyue Li %A Samrachana Adhikari %A Rumi Chunara %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-cho26a %I PMLR %P 394--409 %U https://proceedings.mlr.press/v340/cho26a.html %V 340 %X Continual learning updates models under distribution shift while limiting performance loss on previously seen data, but clinical use also motivates inspectable interfaces that can be compared across model versions. We propose Tree of Concepts, which uses a shallow decision tree to define a fixed vocabulary of root-to-leaf rules and sequentially updates a neural concept predictor and label head with replay. The association between each concept ID and its rule is fixed; predicted concept assignments and the concept-to-label mapping remain adaptive. Across the evaluated health-related tabular protocols, Tree of Concepts attains the highest or tied mean past- and current-slice scores among the compared methods. These comparisons are descriptive point estimates rather than evidence of statistical superiority. Leaf-assignment agreement with the frozen tree ranges from 0.81 to 0.91, indicating that a fixed vocabulary does not imply invariant instance-level explanations. The results motivate further evaluation of fixed rule vocabularies as longitudinally comparable interfaces for continually updated tabular models.
APA
Cho, D., Li, X., Adhikari, S. & Chunara, R.. (2026). Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains. Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:394-409 Available from https://proceedings.mlr.press/v340/cho26a.html.

Related Material