2022-12-31-ren22a.md

abstract	booktitle	title	volume	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	genre	issued	pdf	extras
There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians write notes. We study the automation of medical code prediction using curriculum learning, which is a training strategy for machine learning models that gradually increases the hardness of the learning tasks from easy to difficult. One of the challenges in curriculum learning is the design of curricula – i.e., in the sequential design of tasks that gradually increase in difficulty. We propose Hierarchical Curriculum Learning (HiCu), an algorithm that uses graph structure in the space of outputs to design curricula for multi-label classification. We create curricula for multi-label classification models that predict ICD diagnosis and procedure codes from natural language descriptions of patients. By leveraging the hierarchy of ICD codes, which groups diagnosis codes based on various organ systems in the human body, we find that our proposed curricula improve the generalization of neural network-based predictive models across recurrent, convolutional, and transformer-based architectures. Our code is available at https://github.com/wren93/HiCu-ICD.	Proceedings of the 7th Machine Learning for Healthcare Conference	HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding	182	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	ren22a	0	HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding	198	223	198-223	198	false	Ren, Weiming and Zeng, Ruijing and Wu, Tongzi and Zhu, Tianshu and Krishnan, Rahul G.	given family Weiming Ren given family Ruijing Zeng given family Tongzi Wu given family Tianshu Zhu given family Rahul G. Krishnan	2022-12-31		Proceedings of the 7th Machine Learning for Healthcare Conference	inproceedings	date-parts 2022 12 31	https://proceedings.mlr.press/v182/ren22a/ren22a.pdf