# Classifying Unstructured Clinical Notes via Automatic Weak Supervision

arXiv: https://arxiv.org/abs/2206.12088 · HTML: https://mlsanity.com/paper/2206.12088/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Weakly-Supervised Neural Text Classification](https://mlsanity.com/paper/1809.01478/) (2018)
- [Multimodal Machine Learning for Automated ICD Coding](https://mlsanity.com/paper/1810.13348/) (2018)
- [Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment](https://mlsanity.com/paper/1709.09587/) (2017)
- [Weak Supervision for Affordable Modeling of Electrocardiogram Data](https://mlsanity.com/paper/2201.02936/) (2022)
- [Pairwise Feedback for Data Programming](https://mlsanity.com/paper/1912.07685/) (2019)

## Built on by (most cited first, 7 of 7)

- [Unsupervised Model Selection for Time-series Anomaly Detection](https://mlsanity.com/paper/2210.01078/) (2022)
- [Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models](https://mlsanity.com/paper/2311.00287/) (2023)
- [Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning](https://mlsanity.com/paper/2310.09672/) (2023)
- [ScriptoriumWS: A Code Generation Assistant for Weak Supervision](https://mlsanity.com/paper/2502.12366/) (2025)
- [Can Reasoning LLMs Enhance Clinical Document Classification?](https://mlsanity.com/paper/2504.08040/) (2025)
- [Language Interaction Network for Clinical Trial Approval Estimation](https://mlsanity.com/paper/2405.06662/) (2024)
- [RuCCoD: Towards Automated ICD Coding in Russian](https://mlsanity.com/paper/2502.21263/) (2025)

## Abstract (arXiv)

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.