Textual Data Augmentation for Patient Outcomes Prediction
- URL: http://arxiv.org/abs/2211.06778v1
- Date: Sun, 13 Nov 2022 01:07:23 GMT
- Title: Textual Data Augmentation for Patient Outcomes Prediction
- Authors: Qiuhao Lu, Dejing Dou, Thien Huu Nguyen
- Abstract summary: We propose a novel data augmentation method to generate artificial clinical notes in patients' Electronic Health Records.
We fine-tune the generative language model GPT-2 to synthesize labeled text with the original training data.
We evaluate our method on the most common patient outcome, i.e., the 30-day readmission rate.
- Score: 67.72545656557858
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
We propose a novel EHR data generation model called EHRPD.
It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation.
We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
arXiv Detail & Related papers (2024-06-20T02:20:23Z) - Recent Advances in Predictive Modeling with Electronic Health Records [71.19967863320647]
utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics.
Deep learning has demonstrated its superiority in various applications, including healthcare.
arXiv Detail & Related papers (2024-02-02T00:31:01Z) - MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data
Augmentation [58.93221876843639]
This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion.
It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space.
It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
arXiv Detail & Related papers (2023-10-04T01:36:30Z) - A Comprehensive Benchmark for COVID-19 Predictive Modeling Using
Electronic Health Records in Intensive Care [15.64030213048907]
We propose two clinical prediction tasks, Outcome-specific length-of-stay prediction and Early mortality prediction for COVID-19 patients in intensive care units.
The two tasks are adapted from the naive length-of-stay and mortality prediction tasks to accommodate the clinical practice for COVID-19 patients.
We propose fair, detailed, open-source data-preprocessing pipelines and evaluate 17 state-of-the-art predictive models on two tasks.
arXiv Detail & Related papers (2022-09-16T09:09:15Z) - Unsupervised Pre-Training on Patient Population Graphs for Patient-Level
Predictions [48.02011627390706]
Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging.
In this paper, we apply unsupervised pre-training to heterogeneous, multi-modal EHR data for patient outcome prediction.
We find that our proposed graph based pre-training method helps in modeling the data at a population level.
arXiv Detail & Related papers (2022-03-23T17:59:45Z) - Bridging the Gap Between Patient-specific and Patient-independent
Seizure Prediction via Knowledge Distillation [7.2666838978096875]
Existing approaches typically train models in a patient-specific fashion due to the highly personalized characteristics of epileptic signals.
A patient-specific model can then be obtained with the help of distilled knowledge and additional personalized data.
Five state-of-the-art seizure prediction methods are trained on the CHB-MIT sEEG database with our proposed scheme.
arXiv Detail & Related papers (2022-02-25T10:30:29Z) - Pre-training transformer-based framework on large-scale pediatric claims
data for downstream population-specific tasks [3.1580072841682734]
This study presents the Claim Pre-Training (Claim-PT) framework, a generic pre-training model that first trains on the entire pediatric claims dataset.
The effective knowledge transfer is completed through the task-aware fine-tuning stage.
We conducted experiments on a real-world claims dataset with more than one million patient records.
arXiv Detail & Related papers (2021-06-24T15:25:41Z) - Predicting Clinical Diagnosis from Patients Electronic Health Records
Using BERT-based Neural Networks [62.9447303059342]
We show the importance of this problem in medical community.
We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence.
We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
arXiv Detail & Related papers (2020-07-15T09:22:55Z) - Language Models Are An Effective Patient Representation Learning
Technique For Electronic Health Record Data [7.260199064831896]
We show that patient representation schemes inspired from techniques in natural language processing can increase the accuracy of clinical prediction models.
Such patient representation schemes enable a 3.5% mean improvement in AUROC on five prediction tasks compared to standard baselines.
arXiv Detail & Related papers (2020-01-06T22:24:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.