2023

Does Synthetic Data Generation of LLMs Help Clinical Text Mining?

Tang, Ruixiang, Han, Xiaotian, Jiang, Xiaoqian et al.

Understand

Recent advancements in large language models (LLMs) have led to the development of highly potent models like OpenAI's ChatGPT.

These models have exhibited exceptional performance in a variety of tasks, such as question answering, essay composition, and code generation.
However, their effectiveness in the healthcare sector remains uncertain.
In this study, we seek to investigate the potential of ChatGPT to aid in clinical text mining by examining its ability to extract structured information from unstructured healthcare texts, with a focus on biological named entity recognition and relation extraction.

Built on

Genomics and natural language processing
Yandell MD, Majoros WH · 2002
Earlier work this paper cites.
Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study
Wang X, Hripcsak G, Markatou M, Friedman C · 2009
Earlier work this paper cites.
Realizing the full potential of electronic health records: the role of natural language processing
Ohno-Machado L · 2011
Earlier work this paper cites.
The EU-ADR corpus: annotated drugs, diseases, targets, and their relationships
Van Mulligen EM, Fourrier-Reglat A, Gurwitz D, Molokhia M, Nieto A, Trifiro G, et al · 2012
Earlier work this paper cites.
ChemSpot: a hybrid system for chemical named entity recognition
Rocktäschel T, Weidlich M, Leser U · 2012
Earlier work this paper cites.
Overview of BioNLP shared task 2013
Nédellec C, Bossy R, Kim JD, Kim JJ, Ohta T, Pyysalo S, et al · 2013
Earlier work this paper cites.
NCBI disease corpus: a resource for disease name recognition and concept normalization
Doğan RI, Leaman R, Lu Z · 2014
Earlier work this paper cites.
Chemical named entities recognition: a review on approaches and applications
Eltyeb S, Salim N · 2014
Earlier work this paper cites.
BioCreative V CDR task corpus: a resource for chemical disease relation extraction
Li J, Sun Y, Johnson RJ, Sciaky D, Wei CH, Leaman R, et al · 2016
Earlier work this paper cites.
The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins
Rouillard AD, Gundersen GW, Fernandez NF, Wang Z, Monteiro CD, McDermott MG, et al · 2016
Earlier work this paper cites.
A review of the role of public health informatics in healthcare
Aziz HA · 2017
Earlier work this paper cites.
The eu general data protection regulation (gdpr)
Voigt P, Von dem Bussche A · 2017
Earlier work this paper cites.
EliIE: An open-source information extraction system for clinical trial eligibility criteria
Kang T, Zhang S, Tang Y, Hruby GW, Rusanov A, Elhadad N, et al · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin J, Chang MW, Lee K, Toutanova K · 2018
Earlier work this paper cites.
A medical chatbot
Dharwadkar R, Deshpande NA · 2018
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, et al · 2019
Earlier work this paper cites.

Similar

Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review
Koleck TA, Dreisbach C, Bourne PE, Bakken S · 2019
Cited alongside, same era.
Applications of machine learning in drug discovery and development
Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, et al · 2019
Cited alongside, same era.
BioBERT based named entity recognition in electronic medical record
Yu X, Hu W, Lu S, Sun X, Yuan Z · 2019
Cited alongside, same era.
Named entity recognition for electronic health records: a comparison of rule-based and machine learning approaches
Gorinski PJ, Wu H, Grover C, Tobin R, Talbot C, Whalley H, et al · 2019
Cited alongside, same era.
Named entity recognition using BERT BiLSTM CRF for Chinese electronic health records
Dai Z, Wang X, Ni P, Li Y, Li G, Bai X · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al · 2020
Cited alongside, same era.
An introduction to the california consumer privacy act (CCPA)
Goldman E · 2020
Cited alongside, same era.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al · 2020
Cited alongside, same era.
Exploring chemical space using natural language processing methodologies for drug discovery
Öztürk H, Özgür A, Schwaller P, Laino T, Ozkirimli E · 2020
Cited alongside, same era.
Technical aspects of developing chatbots for medical applications: scoping review
Safi Z, Abd-Alrazaq A, Khalifa M, Househ M, et al · 2020
Cited alongside, same era.
Extracting drug-drug interactions from texts with BioBERT and multiple entity-aware attentions
Zhu Y, Li L, Lu H, Zhou A, Qin X · 2020
Cited alongside, same era.
Electronic medical records and machine learning in approaches to drug development
Shinozaki A · 2020
Cited alongside, same era.

Then

Large Language Models Encode Clinical Knowledge
Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, et al · 2022
Later among the works it cites.
Wordcraft: story writing with large language models
Yuan A, Coenen A, Reif E, Ippolito D · 2022
Later among the works it cites.
Bloom: A 176b-parameter open-access multilingual language model
Scao TL, Fan A, Akiki C, Pavlick E, Ilić S, Hesslow D, et al · 2022
Later among the works it cites.
PaLM: Scaling Language Modeling with Pathways
Original
Chowdhery A, Narang S, Devlin J, Bosma M, Mishra G, Roberts A, et al · 2022
Later among the works it cites.
Available from: https://www.nbcnews.com/tech/tech-news/chatgpt-passes-mba-exam-wharton-professor-rcna67036
Rosenblatt K. ChatGPT passes MBA exam given by a Wharton professor; 2023 · 2023
Closest in time.
ChatGPT Goes to Law School
Choi JH, Hickman KE, Monahan A, Schwarcz D · 2023
Closest in time.
Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning
Baidoo-Anu D, Owusu Ansah L · 2023
Closest in time.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu P, Yuan W, Fu J, Jiang Z, Hayashi H, Neubig G · 2023
Closest in time.
Is ChatGPT a good translator? A preliminary study
Jiao W, Wang W, Huang Jt, Wang X, Tu Z · 2023
Closest in time.
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
Qin C, Zhang A, Zhang Z, Chen J, Yasunaga M, Yang D · 2023
Closest in time.
Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT
Zhong Q, Ding L, Liu J, Du B, Tao D · 2023
Closest in time.
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
Guo B, Zhang X, Wang Z, Jiang M, Nie J, Ding Y, et al · 2023
Closest in time.
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci E, Seßler K, Küchemann S, Bannert M, Dementieva D, Fischer F, et al · 2023
Closest in time.
The promise of large language models in health care
Arora A, Arora A · 2023
Closest in time.
LLaMA: Open and Efficient Foundation Language Models
Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, et al · 2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…