1. Does Synthetic Data Generation
of LLMs Help Clinical Text
Mining?
Authors: Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang, Xia Hu
Presented by Vijitha Gunta
Data Mining, MSSE SJSU
3. Paper Overview
Recent advancements in large language models (LLMs) like
OpenAI's ChatGPT.
Exploration of ChatGPT's effectiveness in clinical text mining.
Focus on biological named entity recognition (NER) and
relation extraction (RE).
Challenges: Poor performance in direct application and privacy
concerns.
Solution: Generating synthetic data with ChatGPT and fine-
tuning local models.
Result: Significant improvement in NER and RE tasks'
performance.
4. GenAI in Healthcare: Paper Objectives
Objectives:
Investigate ChatGPT's
ability for extracting
structured information
from unstructured
healthcare texts.
Focus on tasks of
biological NER and RE.
Overcome performance
limitations and privacy
concerns with LLMs.
Problem Statement:
Effectiveness of LLMs
in Clinical Text Mining. Paper: Assistive Chatbots for healthcare: a succinct review
Paper: ChatGPT in medicine: an overview of its applications, advantages,
limitations, future prospects, and ethical considerations
5. Methodology
METHODOLOGY
OVERVIEW:
ASSESS CHATGPT'S
ZERO-SHOT
PERFORMANCE IN
HEALTHCARE TASKS
(NER & RE).
IDENTIFY
PERFORMANCE
LIMITATIONS AND
PRIVACY ISSUES.
DEVELOP A NEW
TRAINING PARADIGM
USING SYNTHETIC
DATA GENERATION
WITH CHATGPT.
FINE-TUNE A LOCAL
MODEL USING THE
GENERATED
SYNTHETIC DATA.
COMPARE
PERFORMANCE WITH
STATE-OF-THE-ART
(SOTA) MODELS.
6. Key Concepts and Terminologies
Biomedical Named Entity Recognition
(NER):
Identifying and categorizing medical entities
(diseases, symptoms, drugs, etc.) in medical texts.
Biomedical Relation Extraction (RE):
Extracting relationships between medical entities
(diseases and drugs, symptoms and treatments,
etc.).
Zero-Shot Learning:
LLMs’ ability to perform tasks they haven't been
explicitly trained for using prompt-based
instructions.
Synthetic Data Generation: Creating artificial data with ChatGPT to simulate
real healthcare scenarios for model training.
7. Datasets
Datasets for NER Task:
• NCBI Disease Corpus: Contains 6,881 human-labeled
annotations for disease name recognition.
• BioCreative V CDR Corpus (BC5CDR): Includes 1,500
PubMed articles with 4,409 chemicals, 5,818 diseases, and
3,116 chemical-disease interactions annotations for chemical
and disease recognition.
Datasets for RE Task:
• Gene Associations Database (GAD): Comprises 5,330 gene-
disease association annotations from genetic studies.
• EU-ADR Corpus: Contains 100 abstracts with annotations on
relationships between drugs, disorders, and targets.
8. Architecture Overview
• Architecture Components:
•ChatGPT for Synthetic Data
Generation.
•Local Language Model Fine-Tuning.
•Comparative Analysis with SOTA
Models.
• Process Flow:
• ChatGPT generates synthetic data
→ Synthetic data used to fine-tune
local model → Performance
compared with SOTA models.
11. Ablation Studies
and Experiments
• Ablation Studies:
• Evaluating the impact of synthetic data on model performance.
• Experiments Conducted:
•Generating synthetic data using ChatGPT.
•Fine-tuning local models with synthetic vs. real data.
•Performance comparison with SOTA models.
• Evaluation Metrics:
• Precision, Recall, and F1-Score.
• Performance Evaluation:
• Assessing model performance on NER and RE tasks.
• Comparison with zero-shot ChatGPT and SOTA models.
NER
RE
14. Analysis of Generated Texts
• Data Leakage Problem
• Method to Address Leakage
• Findings
• Future Work
15. Key Results
• Significant improvement in F1-score for NER
and RE tasks.
• Synthetic data training outperforms zero-shot
ChatGPT.
• Effectiveness of synthetic data in addressing
performance and privacy issues.
• Comparative analysis highlights the potential
of fine-tuning models with synthetic data.
16. Implications and
Applications
• Implications:
• Enhances the usability of LLMs in healthcare.
• Addresses privacy concerns in clinical data
handling.
• Applications:
• Potential in population health management,
clinical trials, and drug discovery.
• Can facilitate the development of new
treatment plans.
This Photo by Unknown author is licensed under CC BY-NC-ND.
17. Personal Analysis
and insights
• Advances our understanding of LLMs'
applicability in healthcare.
• Innovatively addresses crucial privacy
concerns
• Revolutionize healthcare analytics
• data scarcity
• privacy
18. Advances in Synthetic Data for Data Mining: A Research Overview
Exploring Other Papers
Editor's Notes
Begin by introducing the paper: "Today, I'll be discussing the paper titled 'Does Synthetic Data Generation of LLMs Help Clinical Text Mining?'"
Mention the authors: "This research was conducted by Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang, and Xia Hu."
Provide the publication details: "It was published in March and is a fairly recent paper in a very rapidly evolving and dynamic field of synthetci data and LLMs. It is a well cited and refrenced paper as well, by 34 papers.. "
Rice University; 2 Texas A&M University; 3 University of Texas Health Science Center, School of Biomedical Informatics;
The novelty of this paper lies in its exploration of synthetic data in the healthcare text mining domain, but the crux/differentiating factor is synthetic data. So let's take a look at that.
What is synthetic data and why should you care about it? Definition: Synthetic data is artificially generated data that mimics the characteristics of real-world data. It's created using algorithms and statistical models to simulate the properties and statistical patterns of actual data. In many fields, especially where data privacy is a concern (like healthcare), synthetic data is used for training machine learning models or for testing purposes. The key advantages of synthetic data include:
Privacy and Security: It doesn't contain real user or sensitive information, thereby protecting privacy.
Data Availability and Scalability: Can be generated in large quantities and tailored to specific needs or conditions.
Model Training and Testing: Useful for training machine learning models, especially in situations where real data is scarce or has limitations.
Chatgpt is emerging as a game changer for synthetic data. Because it is capable of generating very high quality data through effective prompting, in large quantities, with very less effort/cost. And let me share an interesting anecdote to emphasise how important this could really be – So we are all mostly familiar with the OAI fiasco that happened just a few weeks ago. It was reported that OAI to overcome training data limitations and is got a the breakthrough in Q*/Q learning models which is what allegedly led to the chaos before sam altman's firing from OAI. So you can clearly see how powerful the effects of synthetic data can be. I've shared here some snippets detailing this report. There is a Forbes article about the Q* breakthrough and OpenAI. There are tweets by famous Silicon Valley AI folks on this matter as well, mentioning synthetic data. For example, I've added a tweet here from Bindu Reddy – an SV founder, she is the CEO of Abacus.AI and previously worked in AI @ Amazon and Google. She says 'As suspected, OAI invented a way to overcome training data limitations with synthetic data When trained with enough examples, models begin to generalize nicely! '
Begin by discussing recent advancements in LLMs: "Large language models, particularly OpenAI's ChatGPT, have shown remarkable capabilities in various tasks. However, their effectiveness in the healthcare sector, specifically in clinical text mining, has been uncertain."
Highlight the study's focus: "This study investigates the potential of ChatGPT in clinical text mining, concentrating on biological named entity recognition and relation extraction tasks."
Address the challenges: "Initial results indicated that direct application of ChatGPT for these tasks was ineffective and raised privacy concerns due to the sensitive nature of patient data."
Introduce the proposed solution: "To address these challenges, the study proposes a novel approach involving the generation of a large volume of high-quality synthetic data using ChatGPT, followed by fine-tuning a local model with this data."
Conclude with the outcomes: "This method significantly improved the performance of downstream tasks, enhancing the F1-score for NER from 23.37% to 63.99% and for RE from 75.86% to 83.59%." Also solved the privacy concern with training on sensitive patient data.
Start with the research problem: "The primary problem addressed in this paper is the effectiveness of Large Language Models, specifically ChatGPT, in the context of clinical text mining. Despite their proven capabilities in various domains, their application in healthcare poses unique challenges."
Discuss papers which talk about state of in Gen AI healthcare: 1. https://arxiv.org/pdf/2308.04178.pdf
The paper titled "Assistive Chatbots for healthcare: a succinct review" by Basabdatta Sen Bhattacharya and Vibhav Sinai Pissurlenkar focuses on the state-of-the-art in AI-enabled Chatbots in healthcare over the last ten years (2013-2023). The paper discusses the potential of these technologies in enhancing human-machine interaction, reducing reliance on human-human interaction, and saving man-hours. However, it also highlights the lack of trust in these technologies regarding patient safety and data protection, as well as limited awareness among healthcare workers. Additionally, the paper notes patients' dissatisfaction with the Natural Language Processing skills of Chatbots compared to humans, emphasizing the need for thorough checks before deploying ChatGPT in assistive healthcare. The review suggests that to enable the deployment of AI-enabled Chatbots in public health services, there is a need to build technology that is simple and safe to use, and to build confidence in the technology among the medical community and patients through focused training and development, as well as outreach.
2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10192861/pdf/frai-06-1169595.pdf The paper titled "ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations" presents a comprehensive analysis of ChatGPT's role in healthcare and medicine. Key points include:
Applications: ChatGPT is used in various medical fields, from aiding in research topic identification to assisting professionals in clinical and laboratory diagnosis. It also helps medical students and healthcare professionals stay updated with new developments.
Capabilities: As a generative pre-trained transformer (GPT) model, ChatGPT effectively captures human language nuances, generating contextually relevant responses across a broad range of prompts.
Virtual Assistance: Development of virtual assistants using ChatGPT to aid patients in health management.
Ethical and Legal Concerns: Use of ChatGPT and AI in medical writing raises issues like copyright infringement, medico-legal complications, and the need for transparency in AI-generated content.
Limitations and Considerations: Despite its potential, the use of ChatGPT in medicine comes with limitations and requires careful consideration of ethical aspects .
Elaborate on objectives:
"The first objective is to explore how well ChatGPT can extract structured information, such as entities and their relationships, from unstructured healthcare texts."
"Specifically, the study focuses on biological named entity recognition and relation extraction, which are crucial for understanding and processing medical data."
"The research aims to address two main issues: the inherent performance limitations of ChatGPT when applied directly to healthcare data and the significant privacy concerns that arise from handling sensitive patient information."
Conclude: "Overall, the study seeks to enhance the practicality and safety of using ChatGPT in healthcare, contributing to its broader applicability in the field."
Initial Assessment:
Context: The research initially focused on evaluating ChatGPT's zero-shot performance in tasks specific to the healthcare domain, particularly in named entity recognition and relation extraction. This was done by testing ChatGPT’s ability to process unstructured healthcare texts and extract meaningful information like medical entities and their relationships.
Identified Issues:
Context: The study discovered that ChatGPT, when directly applied to healthcare tasks, showed poor performance in comparison to specialized state-of-the-art models. Moreover, significant privacy concerns arose, particularly regarding the risk of exposing sensitive patient data, which is a critical issue in healthcare.
Proposed Solution:
Context: To tackle the identified challenges, the paper proposes a new approach. This method involves using ChatGPT to generate large volumes of synthetic data, simulating real healthcare scenarios but without using actual patient data. This synthetic data generation aims to create a rich and diverse dataset for model training.
Fine-Tuning Process:
Context: The research then focused on fine-tuning a local language model with the generated synthetic data. This step was crucial to adapt the model specifically for healthcare tasks, improving its performance in extracting and processing medical information from texts.
Comparative Analysis:
Context: Finally, the study conducted a comparative analysis to evaluate the effectiveness of the fine-tuned model. This involved comparing the performance of the new model, trained on synthetic data, against state-of-the-art models trained on real datasets. The comparison aimed to assess improvements in accuracy and effectiveness in healthcare-specific tasks.
NER: "In the context of this research, biomedical Named Entity Recognition involves the process of identifying and categorizing various medical entities, like diseases, symptoms, and drugs, within a given medical text. This is crucial for structuring unstructured healthcare data."
RE: "Relation Extraction in this study refers to the task of identifying and extracting relationships between different medical entities. For example, understanding how a certain drug affects a particular disease, which is vital for extracting meaningful insights from medical texts."
Zero-Shot Learning: "A key aspect of this research is zero-shot learning, a capability of large language models like ChatGPT. It enables them to perform tasks without prior explicit training, using only instructional prompts. This is particularly important for adapting ChatGPT to new tasks like healthcare text analysis."
Synthetic Data: "The paper emphasizes synthetic data generation, where ChatGPT is used to create artificial, yet realistic, healthcare data. This approach helps in training models without compromising patient privacy and bypasses the challenges of limited real healthcare datasets."
Datasets for NER Task:
NCBI Disease Corpus: Contains 6,881 human-labeled annotations for disease name recognition.
BioCreative V CDR Corpus (BC5CDR): Includes 1,500 PubMed articles with 4,409 chemicals, 5,818 diseases, and 3,116 chemical-disease interactions annotations for chemical and disease recognition.
Datasets for RE Task:
Gene Associations Database (GAD): Comprises 5,330 gene-disease association annotations from genetic studies.
EU-ADR Corpus: Contains 100 abstracts with annotations on relationships between drugs, disorders, and targets.
Data Quality and Annotation:
GAD and EU-ADR are considered weakly supervised datasets with noisy labels.
To improve accuracy, three annotators manually labeled 200 data samples from GAD and EU-ADR test datasets.
Ground truth labels were determined through majority voting.
ChatGPT's Role in Synthetic Data Generation:
Context: The paper describes using ChatGPT to generate a large volume of synthetic data, which is critical for training models in healthcare applications. This data generation involves creating varied examples with different sentence structures and linguistic patterns. A significant aspect of this process is the generation of data that's representative of real-world healthcare scenarios, but without using actual patient data, thus addressing privacy concerns.
ChatGPT shows average performance in biomedical relation extraction and poor performance in named entity recognition tasks.
Privacy Concerns:
Directly uploading patient data poses significant privacy concerns, violating regulations like GDPR and CCPA.
Proposed Solution:
Use ChatGPT to generate a large volume of training data with labels for local model training.
This approach solves the low-resource issue common in healthcare data.
Advantages of Local Models:
Addresses privacy concerns as synthetic data doesn't contain patient-sensitive information.
Enables hospitals to use local models for healthcare tasks while protecting patient data privacy.
Fine-Tuning the Local Language Model:
Context: The study highlights the process of fine-tuning a local pre-trained language model with the generated synthetic data. This process is essential to adapt the model for specific tasks in healthcare, such as NER and RE. The synthetic data, created to mimic real healthcare texts, provides a rich and varied dataset for effectively training the local model, enhancing its performance and suitability for healthcare applications.
Comparative Analysis with SOTA Models:
Context: The paper emphasizes the importance of comparing the performance of the fine-tuned local model with state-of-the-art models. This comparison is vital to assess the efficacy of the synthetic data training approach. The study demonstrates that the fine-tuned model, using synthetic data generated by ChatGPT, shows significant improvement in performance compared to the zero-shot capabilities of ChatGPT and, in some cases, achieves comparable results to models trained on actual datasets.
The paper describes a method for generating synthetic data using ChatGPT to improve performance in biomedical tasks. The process involved:
Designing prompts inspired by ChatGPT for data generation.
Creating and evaluating data samples using these prompts, refining them over three rounds to find the optimal prompt. They generated 10 data samples using each prompt and manually compared their quality to select the best prompt.
Ensuring high quality of synthetic data by mimicking the style of PubMed Journal articles and using different seeds to prevent duplication.
Using specific entity seeds for named entity recognition and formatted examples from original datasets for relation extraction tasks.
The result was synthetic data fluent and similar to scientific articles, enhancing the performance in biomedical tasks while addressing privacy concerns.
Example of generated texts.
Ablation Study Overview:
Context: The paper's ablation studies focus on assessing how synthetic data generation impacts the model’s performance. Specifically, these studies analyze the effectiveness of ChatGPT-generated synthetic data in improving the model's ability to accurately perform NER and RE tasks. The variations in synthetic data inputs, like different sentence structures and linguistic patterns, allow for a thorough evaluation of their impact on model performance.
Methodology for NER: The approach involved extracting seed entities from the training set to generate synthetic sentences with entity annotations, setting the number of sentences per entity to 30.
Model Fine-Tuning: Three pre-trained language models (BERT, RoBERTa, BioBERT) were fine-tuned using the synthetic dataset.
Evaluation Metrics: Performance was evaluated using precision, recall, and F1 scores, comparing zero-shot ChatGPT, models fine-tuned on synthetic data, and models fine-tuned on original training sets.
Significant Improvements: Fine-tuning on synthetic data led to substantial improvements in all metrics over the zero-shot scenario, with BERT showing more than 35% improvement in precision, recall, and F1 scores compared to ChatGPT.
Comparable Performance: In some cases, models fine-tuned on synthetic data achieved performance comparable to those fine-tuned on original datasets.
Impact of Synthetic Sentence Quantity: Experiments showed that increasing the number of synthetic sentences improved model performance up to a point, after which improvements were marginal. Adjusting the ratio of synthetic to real entities also enhanced performance, particularly for under-represented entities.
Methodology: The study followed the outlined methodology, sampling three positive and negative examples from a labeled dataset as seeds. For each round, three positive and negative sentences were generated, accumulating 6437 and 6424 examples for GAD and EU-ADR datasets, respectively.
Model Fine-Tuning and Evaluation: Models (BERT, RoBERTa, BioBERT) were fine-tuned using synthetic data and evaluated on precision, recall, and F1 scores, comparing zero-shot ChatGPT, models fine-tuned with synthetic data, and models fine-tuned on original datasets.
Notable Improvements: Fine-tuning on synthetic data showed significant improvements in all metrics over zero-shot performance, with average improvements exceeding 6% in precision, 10% in recall, and 8% in F1 scores.
Comparative Performance: The models trained on synthetic data achieved performance comparable to those fine-tuned on original datasets. Specifically, for the GAD dataset, the synthetic data-trained model outperformed the original dataset.
Impact of Synthetic Sentence Quantity: Experiments indicated that the number of synthetic sentences positively impacts model performance up to a certain threshold. Optimal results were achieved with around 3500 synthetic sentences, and using 80 seed examples was found sufficient for enhancing data quality and diversity.
Avoiding Duplication: Not using seed examples resulted in duplicated synthetic data, significantly dropping model performance.
Concern about Data Leakage: Since ChatGPT is trained on publicly available datasets, there's a concern that it might inadvertently leak information from the datasets used in the experiments.
Method to Address Leakage: To mitigate this, the researchers used a sentence transformer to obtain embeddings of both original and synthetic data, which were then analyzed using T-SNE.
Findings from Data Analysis: The T-SNE analysis showed distinct patterns between synthetic and original data, suggesting that ChatGPT did not simply memorize and reproduce the dataset.
Future Work: The researchers plan to explore methods to generate synthetic data that more closely matches the distribution of the original data, further minimizing the risk of data leakage.
Summarizing Key Results:
Context: The paper reports significant improvements in the F1-score for both NER and RE tasks when using models fine-tuned with synthetic data. For instance, the F1-score for NER improved from 23.37% to 63.99%, and for RE from 75.86% to 83.59%. These results significantly surpass the zero-shot performance of ChatGPT, demonstrating the effectiveness of the synthetic data approach.
Discussing the Implications:
Context: The study underscores the effectiveness of using synthetic data to address both performance limitations in healthcare tasks and privacy concerns. By generating data using ChatGPT, the approach eliminates the need for real patient data, thereby safeguarding privacy. The performance gains observed in comparison to SOTA models highlight the practical potential of this methodology in enhancing LLMs' applicability to clinical text mining tasks, offering a promising avenue for future healthcare-related NLP applications.
Speaker Notes
Discussing Implications: "This research has significant implications for the healthcare industry. It demonstrates a way to enhance the usability of large language models in clinical settings, particularly by addressing key privacy concerns associated with patient data."
Highlighting Applications: "The applications of this research are far-reaching. It can significantly contribute to population health management, aid in clinical trials, and be pivotal in drug discovery processes. Furthermore, this approach can facilitate the development of new treatment plans, leveraging the advanced capabilities of LLMs in processing and analyzing medical data."
Personal Analysis: "In my analysis, this study not only advances our understanding of LLMs' applicability in healthcare but also innovatively addresses crucial privacy concerns. The use of synthetic data as a training tool is a significant leap in ensuring patient data privacy while leveraging AI's capabilities."
Sharing Insights: "What I find most intriguing is the potential of this methodology to revolutionize healthcare analytics. The ability to generate and use synthetic data could be a game-changer in how we approach data scarcity and privacy in healthcare research and applications."
Wrapping Up: "To conclude, this study presents a compelling solution for enhancing the performance of large language models in healthcare-specific tasks. It successfully addresses the dual challenges of performance enhancement and data privacy."
Emphasizing Significance: "The approach outlined in this paper holds significant potential for future research and practical applications in healthcare, demonstrating a promising path for the integration of advanced AI tools in this critical sector."
[Add data from other papers on synthetic data?] [pictures & visualisations from other papers?]
https://arxiv.org/pdf/2302.04062.pdf
Definition and Importance of Synthetic Data: Synthetic data is defined as artificially generated data that simulates real-world data. This type of data is particularly important in fields where data privacy is crucial, like healthcare.
Advantages of Synthetic Data:
Privacy and Security: Synthetic data doesn't contain real user information, enhancing privacy.
Data Availability and Scalability: It can be generated in large quantities and tailored for specific needs.
Model Training and Testing: Synthetic data is valuable for training machine learning models, especially when real data is limited or has restrictions.
ChatGPT's Role in Synthetic Data Generation: ChatGPT is highlighted as a game-changer in synthetic data generation due to its ability to produce high-quality data efficiently and with minimal effort or cost.
Case Study - OpenAI's Use of Synthetic Data: The paper mentions an incident involving OpenAI (OAI) and a breakthrough in Q*/Q learning models, underscoring the powerful impact of synthetic data.
Applications across Fields: The paper explores the use of synthetic data in various domains, including healthcare, business, education, and AI-generated content, detailing the challenges and opportunities in these areas.
Challenges and Future Research: The paper identifies key challenges in synthetic data generation, such as evaluation metrics, addressing biases in underlying models, and ensuring data quality. It suggests that future research should focus on improving these aspects.
Trustworthiness of Synthetic Data: The paper discusses the need for synthetic data to be a reliable representation of real data, emphasizing the importance of maintaining privacy, preventing biases, and ensuring data accuracy.
