Library for clinical NLP with spaCy.
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Updated
Oct 31, 2024 - Jupyter Notebook
Library for clinical NLP with spaCy.
Medical Question Answering Dataset of 47,457 QA pairs created from 12 NIH websites
A novel medical large language model family with 13/70B parameters, which have SOTA performances on various medical tasks
Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation
Multitask Learning with Pretrained Transformers
[ACL 2024 Findings] This is the code for our paper "Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models".
Code for "Retrieve, Reason, and Refine: Generating Accurate and Faithful Discharge/Patient Instructions" (NeurIPS 2022)
Discovering Related Clinical Concepts using Large Amounts of Clinical Notes. An unsupervised graphical approach to mine related concepts by leveraging the volume within large amounts of clinical notes.
A rule-based Python module for spitting documents into sections.
Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text, ClinicalNLP workshop at EMNLP 2020
The project is in the incubation stage and still under development. ForteHealth is a flexible and powerful ML workflow builder for biomedical and clinical scenarios. This is part of the CASL project: https://casl-project.ai/
Instructions and code to create for a table of UMLS, SNOMED or HPO concepts containing Dutch medical names, usable in named entity recognition and linking methods such MedCAT.
Official repo for "Characterizing Stigmatizing Language in Medical Records" (ACL 2023)
Detecting the functioning level of a patient from a free-text clinical note in Dutch.
A Python implementation of the ConText algorithm for clinical text concept assertion using the spaCy framework
AskDocs: A medical QA dataset
Estimate sentiment in clinical notes via keywords or deep learning models
MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain (ACL-IJCNLP '21)
SpaCy component for modifying the string of a doc before tokenizing.
In the Name of Fairness: Assessing the Bias in Clinical Record De-identification
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