We introduce 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.
This work, Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation, has been accepted by JMIR recently!
Ascle consists of three modules:
🌟 Generative Functions: For the first time, Ascle includes four advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation;
Basic NLP Functions: Ascle consists of 12 essential NLP functions such as word tokenization and sentence segmentation;
Query and Search Capabilities: Ascle provides user-friendly query and search functions on clinical databases.
⚙️indicates that we have our fine-tuned models for this particular task.
⭐️indicates that we conducted evaluations for this particular task.
15_10_2024 - Added new codes integrating LLMs such as ChatGPT, Gemini, Claude, and LLaMA into Ascle.
29_07_2024 - We uploaded a new folder, Ascle-JPBench, containing open-sourced EN-JP medical task data examples. Ascle-JPBench will support comprehensive tasks such as QA, NLI, and multiple choice.
17_05_2024 - We are currently updating Ascle. In the next version, Ascle will include the question-answering task based on the RAG framework and will support multiple languages for all tasks.
07_11_2023 - New Release v2.2: we changed the toolkit name to Ascle from EHRKit, easier to use!
10_07_2023 - New Release v2.0: a large re-organization and improvement from v1.0.
24_05_2023 - New Release Pretrained Models for Machine Translation.
15_03_2022 - Merged the ehrkit folder to support off-shelf medical text processing.
10_03_2022 - Made all tests available in an ipynb file and updated the most recent version.
17_12_2021 - New folder collated_tasks containing Fall 2021 functionalities added
11_05_2021 - cleaned up the notebooks, fixed up the readme using depth=1.
04_05_2021 - Tests run-through added in tests.
22_04_2021 - Freezing development.
22_04_2021 - Completed the tutorials and readme.
20_04_2021 - Spring functionality finished -- mimic classification, summarization, and query extraction.
You can download Ascle as a git repository; simply clone to your choice of directories (keep depth small to keep the old versions out and reduce size).
git clone https://github.com/Yale-LILY/Ascle.git
cd Ascle
python3 -m venv asclevir/
source asclevir/bin/activate
pip install -r requirements.txt
NOTE: there is a chance that your Python version is not compatible with scispacy, so you can install with the following command:
pip install scispacy
pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.0/en_core_sci_sm-0.5.0.tar.gz
Then you are good to go!
We provide various generative functions and basic NLP functions. A quick start is to run the demo.py:
cd Ascle
python demo.py
Note: this may take some time, as some packages will be downloaded.
from Ascle import Ascle
# create Ascle
med = Ascle()# Text Simplification
main_record = """
The patient presents with symptoms of acute bronchitis,
including cough, chest congestion, and mild fever.
Auscultation reveals coarse breath sounds and occasional
wheezing. Based on the clinical examination, a diagnosis
of acute bronchitis is made, and the patient is prescribed
a short course of bronchodilators and advised to rest and
stay hydrated.
"""
# choose the model
layman_model = "ireneli1024/bart-large-elife-finetuned"
med.update_and_delete_main_record(main_record)
# call the text simplification function and print the output
print(med.get_layman_text(layman_model, min_length=20, max_length=70))
>> """
The patient presents with symptoms of acute bronchitis including
cough, chest congestion and mild fever. Auscultation reveals coarse
breath sounds and occasional wheezing. Based on these symptoms and
the patient's history of previous infections with the same condition,
the doctor decides that the patient is likely to have a cold or bronch.
"""main_record = """
Myeloid derived suppressor cells (MDSC) are immature myeloid
cells with immunosuppressive activity. They accumulate in
tumor-bearing mice and humans with different types of cancer,
including hepatocellular carcinoma (HCC).
"""
med.update_and_delete_main_record(main_record)
# call the machine translation function and print the output
print(med.get_translation_mt5("French"))
>> """
Les cellules suppressives dérivées de myéloïdes (MDSC) sont des
cellules myéloïdes immatures ayant une activité immunosuppressive,
accumulées chez des souris et des humains ayant différents types de
cancer, y compris le carcinome hépatocellulaire (HCC).
"""main_record = """
summarize this text:
Neurons (also called neurones or nerve cells) are the fundamental units of the brain and nervous system,
the cells responsible for receiving sensory input from the external world, for sending motor commands to
our muscles, and for transforming and relaying the electrical signals at every step in between. More than
that, their interactions define who we are as people. Having said that, our roughly 100 billion neurons do
interact closely with other cell types, broadly classified as glia (these may actually outnumber neurons,
although it’s not really known)
"""
med.update_and_delete_main_record(main_record)
# call the GPT function and print the output
print(med.call_GPT(api_key="xxxx"))
>> """
Neurons, or nerve cells, are essential units of the brain and nervous
system responsible for receiving sensory input, sending motor commands
to muscles and processing electrical signals. They also play a significant
role in defining human personality. Despite being approximately 100 billion
in number, neurons often interact with other cell types known as glia, which
may outnumber neurons.
"""
med.update_and_delete_main_record(main_record)
# call the Claude function and print the output
print(med.call_Claude(api_key="xxxx"))
>> """
Here is a summary of the text:
- Neurons are the basic cells of the brain and nervous system.
- They receive sensory input, send motor signals to muscles, and relay electrical signals in between.
- Neuron interactions shape our identity and personality.
- There are about 100 billion neurons in the human brain.
- Neurons interact with glial cells, which may outnumber neurons.
"""
med.update_and_delete_main_record(main_record)
# call the Gemini function and print the output
print(med.call_Gemini(api_key="xxxx"))
>> """
Neurons, the building blocks of the brain and nervous system,
are responsible for receiving sensory information, sending motor
signals, and transmitting electrical signals throughout the
body. Their intricate interactions shape our identities. While
there are approximately 100 billion neurons, they work closely
with glial cells, which may even outnumber neurons but whose
exact quantity is unknown.
"""
med.update_and_delete_main_record(main_record)
# call the LlaMa function and print the output
print(med.call_LlaMa(api_key="xxxx"))
>> """
Neurons are specialized cells that have evolved to process information and transmit it to other cells or
parts of the body. They have three main parts: the dendrites, the cell body, and the axon. Dendrites are
branched, tree-like structures that receive signals from other neurons. The cell body, also called the
soma, contains the nucleus and the rest of the cell's organelles. The axon is a long, thin structure that
carries signals away from the cell body and to other neurons or to muscles or glands. Neurons are capable of transmitting signals electrically, chemically, or both. They can also store
information in the form of electrical or chemical changes in their membranes. This information can be retrieved and used later to influence the neuron'
"""# Load UmlsQA
from umls_qa import UmlsQA
# Initialize the UmlsQA
med = UmlsQA(model_name="gpt-3.5-turbo", api_key="xxxx")
# Define the medical question in a variable
question = "How does smoking affect lung function?"
# Print the response in English
print(med.ask_medical_question(question))
>> """
Smoking can significantly impact lung function by causing inflammation
and damage to the airways and alveoli. This can lead to conditions such
as chronic obstructive pulmonary disease (COPD) and emphysema, which can
result in difficulty breathing and reduced lung capacity. Smoking also
increases the risk of developing lung cancer. It is important to quit smoking
to protect your lung health and overall well-being.
"""In Ascle, users can access any publicly available language model. Additionally, we provide users with 32 of our fine-tuned models which are suitable for multiple-choice QA, text simplification, and machine translation tasks.
Please feel to download our fine-tuned models:
| Tasks | Base Model | Fine-Tuned Data | Huggingface Link |
|---|---|---|---|
| Multi-choice QA | BioBERT | HEADQA | Download |
| ClinicalBERT | HEADQA | Download | |
| SapBERT | HEADQA | Download | |
| PubMedBERT | HEADQA | Download | |
| GatorTron | HEADQA | Download | |
| BioBERT | MedMCQA-w-context | Download | |
| ClinicalBERT | MedMCQA-w-context | Download | |
| SapBERT | MedMCQA-w-context | Download | |
| PubMedBERT | MedMCQA-w-context | Download | |
| GatorTron | MedMCQA-w-context | Download | |
| BioBERT | MedMCQA-wo-context | Download | |
| ClinicalBERT | MedMCQA-wo-context | Download | |
| SapBERT | MedMCQA-wo-context | Download | |
| PubMedBERT | MedMCQA-wo-context | Download | |
| GatorTron | MedMCQA-wo-context | Download | |
| Text Simplification | BART | eLife | Download |
| BioBART | eLife | Download | |
| BigBirdPegasus | eLife | Download | |
| BART | PLOS | Download | |
| BioBART | PLOS | Download | |
| BigBirdPegasus | PLOS | Download | |
| Machine Translation | mT5 | UFAL (en_es) | Download |
| mT5 | UFAL (en_fr) | Download | |
| mT5 | UFAL (en_ro) | Download | |
| mT5 | UFAL (en_cs) | Download | |
| mT5 | UFAL (en_de) | Download | |
| mT5 | UFAL (en_hu) | Download | |
| mT5 | UFAL (en_pl) | Download | |
| mT5 | UFAL (en_sv) | Download | |
| MarianMT | UFAL (en_es) | Download | |
| MarianMT | UFAL (en_fr) | Download | |
| MarianMT | UFAL (en_ro) | Download |
Please create a GitHub issue if you have any questions, suggestions, requests or bug-reports. We welcome PRs!
This project started at the year of 2018. There are many people participated and made contributions:
Rui Yang*, Qingcheng Zeng*, Keen You*, Yujie Qiao*, Lucas Huang, Chia-Chun Hsieh, Benjamin Rosand, Jeremy Goldwasser, Amisha D Dave, Tiarnan D.L. Keenan, Emily Y Chew, Dragomir Radev, Zhiyong Lu, Hua Xu, Qingyu Chen, Irene Li
Our sincere gratitude also goes to Dr.Edison Marrese-Taylor and Prof. Yutaka Matsuo from the University of Tokyo, for their invaluable guidance and support throughout this project.
We also acknowledge external collaborators, including Mónica Pina-Navarro (University of Alicante), who contributed during a research stay.
🕯️ Especially in the memory of Prof. Dragomir Radev, who has dedicated so much to this project.
Please find our paper at https://arxiv.org/abs/2311.16588.
@misc{yang2023ascle,
title={Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation},
author={Rui Yang and Qingcheng Zeng and Keen You and Yujie Qiao and Lucas Huang and Chia-Chun Hsieh and Benjamin Rosand and Jeremy Goldwasser and Amisha D Dave and Tiarnan D. L. Keenan and Emily Y Chew and Dragomir Radev and Zhiyong Lu and Hua Xu and Qingyu Chen and Irene Li},
year={2023},
doi={10.2196/60601},
eprint={2311.16588},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
We will continue to maintain and update this repository. If you have any questions, feel free to contact us.
Rui Yang: yang_rui@u.nus.edu
Dr. Irene Li: ireneli@ds.itc.u-tokyo.ac.jp