zonghui0228/chip2019task3
第五届中国健康信息处理会议（CHIP2019）- 评测三：临床试验筛选标准短文本分类
This project helps clinical researchers and trial coordinators efficiently identify suitable participants for clinical trials. It takes unstructured Chinese text descriptions of patient eligibility criteria (like 'age > 80 years' or 'history of recent intracranial surgery') and automatically categorizes them into 44 predefined semantic types, such as 'Age' or 'Therapy or Surgery'. The output can then be used to automate the comparison of patient records against trial criteria, speeding up recruitment.
No commits in the last 6 months.
Use this if you need to quickly and accurately classify free-text clinical trial eligibility criteria to streamline patient recruitment.
Not ideal if your clinical trial criteria are not in Chinese or if you need to classify full patient medical records rather than just individual criteria descriptions.
Stars
41
Forks
11
Language
Python
License
—
Category
Last pushed
May 12, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/zonghui0228/chip2019task3"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
medspacy/medspacy
Library for clinical NLP with spaCy.
jamesmullenbach/caml-mimic
multilabel classification of EHR notes
ncbi-nlp/NegBio
:newspaper: High-performance tool for negation and uncertainty detection in radiology reports
bionlplab/radtext
Python Radiology Text Analysis System
ClarityNLP/ClarityNLP
An NLP framework for clinical phenotyping. Docker | Python | Solr | OMOP....