# Textual Data Augmentation for Patient Outcomes Prediction

arXiv: https://arxiv.org/abs/2211.06778 · HTML: https://mlsanity.com/paper/2211.06778/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [Generative Data Augmentation for Commonsense Reasoning](https://mlsanity.com/paper/2004.11546/) (2020)
- [DARE: Data Augmented Relation Extraction with GPT-2](https://mlsanity.com/paper/2004.13845/) (2020)
- [On Data Augmentation for Extreme Multi-label Classification](https://mlsanity.com/paper/2009.10778/) (2020)
- [Data Augmentation for Spoken Language Understanding via Pretrained Language Models](https://mlsanity.com/paper/2004.13952/) (2020)
- [Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution](https://mlsanity.com/paper/2201.02510/) (2021)
- [Learning Conceptual-Contextual Embeddings for Medical Text](https://mlsanity.com/paper/1908.06203/) (2019)

## Built on by (most cited first, 6 of 6)

- [A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives](https://mlsanity.com/paper/2506.16594/) (2025)
- [Zero-shot and Few-shot Generation Strategies for Artificial Clinical Records](https://mlsanity.com/paper/2403.08664/) (2024)
- [Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language Modeling](https://mlsanity.com/paper/2409.09831/) (2024)
- [Predicting Visit Cost of Obstructive Sleep Apnea using Electronic Healthcare Records with Transformer](https://mlsanity.com/paper/2301.12289/) (2023)
- [Evaluation Metrics for Text Data Augmentation in NLP](https://mlsanity.com/paper/2402.06766/) (2024)
- [Latent Feature Mining for Predictive Model Enhancement with Large Language Models](https://mlsanity.com/paper/2410.04347/) (2024)

## Abstract (arXiv)

Deep learning models have demonstrated superior performance in various healthcare applications. However, the major limitation of these deep models is usually the lack of high-quality training data due to the private and sensitive nature of this field. In this study, we propose a novel textual data augmentation method to generate artificial clinical notes in patients' Electronic Health Records (EHRs) that can be used as additional training data for patient outcomes prediction. Essentially, we fine-tune the generative language model GPT-2 to synthesize labeled text with the original training data. More specifically, We propose a teacher-student framework where we first pre-train a teacher model on the original data, and then train a student model on the GPT-augmented data under the guidance of the teacher. We evaluate our method on the most common patient outcome, i.e., the 30-day readmission rate. The experimental results show that deep models can improve their predictive performance with the augmented data, indicating the effectiveness of the proposed architecture.