Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 44 of its 76 references that are in the map. First posted on arXiv 28 Nov 2023.
Builds on
44 of 76 references.
References · 44
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
- 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
- 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
- 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
- 2020Big Bird: Transformers for Longer Sequencescited by 1,684
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
- 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
- 2019Publicly Available Clinical BERT Embeddingscited by 1,002
- 2023Large Language Modelscited by 780
- 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 543
32 more
- 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 478
- 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 384
- 2023MEDITRON-70B: Scaling Medical Pretraining for Large Language Modelscited by 274
- 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
- 2020Self-Alignment Pretraining for Biomedical Entity Representationscited by 237
- 2018Marian: Fast Neural Machine Translation in C++cited by 203
- 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 163
- 2023HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledgecited by 162
- 2017Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysiscited by 148
- 2019A Question-Entailment Approach to Question Answeringcited by 134
- 2019MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIIcited by 122
- 2023Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Healthcited by 118
- 2019HEAD-QA: A Healthcare Dataset for Complex Reasoningcited by 101
- 2022ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reportscited by 101
- 2021SciFive: a text-to-text transformer model for biomedical literaturecited by 99
- 2023ExpertQA: Expert-Curated Questions and Attributed Answerscited by 97
- 2022BioBART: Pretraining and Evaluation of A Biomedical Generative Language Modelcited by 83
- 2019ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Networkcited by 73
- 2021PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarizationcited by 62
- 2023MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Recordscited by 56
- 2022Making Science Simple: Corpora for the Lay Summarisation of Scientific Literaturecited by 55
- 2021Paragraph-level Simplification of Medical Textscited by 53
- 2018BioSentVec: creating sentence embeddings for biomedical textscited by 47
- 2021Launching into clinical space with medspaCy: a new clinical text processing toolkit in Pythoncited by 40
- 2020Question-Driven Summarization of Answers to Consumer Health Questionscited by 39
- 2021Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Reviewcited by 35
- 2021Hierarchical Learning for Generation with Long Source Sequencescited by 32
- 2018Attention-based Neural Text Segmentationcited by 26
- 2023Integrating UMLS Knowledge into Large Language Models for Medical Question Answeringcited by 10
- 2019A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguationcited by 8
- 2023NLPBench: Evaluating Large Language Models on Solving NLP Problemscited by 8
- 2023A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Modelscited by 3
Built on by
Citing papers per year: 2024: 3.
3 papers.
- 2024Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Biascited by 20
- 2024Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Educationcited by 9
- 2024AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Datacited by 8
- 2024AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Data15 Oct 2024
- 2024Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education15 Jul 2024
- 2024Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias26 Jan 2024