Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
arXiv:2311.16588
Abstract
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcare professionals with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle evaluates and provides interfaces for the latest pre-trained language models, encompassing four advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. The toolkit, its models, and associated data are publicly available via https://github.com/Yale-LILY/MedGen.
5 figures, 4 tables
References in corpus (23)
- BioBERT: a pre-trained biomedical language representation model for biomedical text mining
- Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
- Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
- PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
- Publicly Available Clinical BERT Embeddings
- Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health
- Towards Expert-Level Medical Question Answering with Large Language Models
- A Question-Entailment Approach to Question Answering
- MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
- MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
- HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge
- SciFive: a text-to-text transformer model for biomedical literature
- MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
- Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences
- GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
- ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation
- Hierarchical Learning for Generation with Long Source Sequences
- A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics
- A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation
- A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models
- Integrating UMLS Knowledge into Large Language Models for Medical Question Answering
- Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts
- NLPBench: Evaluating Large Language Models on Solving NLP Problems