2022-12-31-gao22a.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
Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes. Due to the unstructured nature of these narratives, providers employ dedicated staff to assign diagnostic codes to patients’ diagnoses using the International Classification of Diseases (ICD) coding system. This manual process is not only time-consuming but also costly and error-prone. Prior work demonstrated potential utility of Machine Learning (ML) methodology in automating this process, but it has relied on large quantities of manually labeled data to train the models. Additionally, diagnostic coding systems evolve with time, which makes traditional supervised learning strategies unable to generalize beyond local applications. In this work, we introduce a general weakly-supervised text classification framework that learns from class-label descriptions only, without the need to use any human-labeled documents. It leverages the linguistic domain knowledge stored within pre-trained language models and the data programming framework to assign code labels to individual texts. We demonstrate the efficacy and flexibility of our method by comparing it to state-of-the-art weak text classifiers across four real-world text classification datasets, in addition to assigning ICD codes to medical notes in the publicly available MIMIC-III database.	Proceedings of the 7th Machine Learning for Healthcare Conference	Classifying Unstructured Clinical Notes via Automatic Weak Supervision	182	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	gao22a	0	Classifying Unstructured Clinical Notes via Automatic Weak Supervision	673	690	673-690	673	false	Gao, Chufan and Goswami, Mononito and Chen, Jieshi and Dubrawski, Artur	given family Chufan Gao given family Mononito Goswami given family Jieshi Chen given family Artur Dubrawski	2022-12-31		Proceedings of the 7th Machine Learning for Healthcare Conference	inproceedings	date-parts 2022 12 31	https://proceedings.mlr.press/v182/gao22a/gao22a.pdf