A fast implementation of RuSH (Rule-based sentence Segmenter using Hashing).
Project description
PyRuSH
PyRuSH is the python implementation of RuSH, which is orginally developed using Java. RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy.
If you wish to cite RuSH in a publication, please use:
Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587.
The full text can be found at https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616
Installation:
pip install PyRuSH
How to use:
A standalone RuSH class is available to be directly used in your code.
from PyRuSH.RuSH import RuSH
input_str = "The patient was admitted on 03/26/08\n and was started on IV antibiotics elevation" +\
", was also counseled to minimizing the cigarette smoking. The patient had edema\n\n" +\
"\n of his bilateral lower extremities. The hospital consult was also obtained to " +\
"address edema issue question was related to his liver hepatitis C. Hospital consult" +\
" was obtained. This included an ultrasound of his abdomen, which showed just mild " +\
"cirrhosis. "
rush = RuSH('../conf/rush_rules.tsv')
sentences=rush.segToSentenceSpans(input_str)
for sentence in sentences:
print('Sentence({0}-{1}):\t>{2}<'.format(sentence.begin, sentence.end, input_str[sentence.begin:sentence.end]))
PyRuSH is the python implementation of RuSH, which is orginally developed using Java. RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy.
If you wish to cite RuSH in a publication, please use:
Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587.
The full text can be found at https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616
Installation:
pip install PyRuSH
How to use:
A standalone RuSH class is available to be directly used in your code.
from PyRuSH.RuSH import RuSH
input_str = "The patient was admitted on 03/26/08\n and was started on IV antibiotics elevation" +\
", was also counseled to minimizing the cigarette smoking. The patient had edema\n\n" +\
"\n of his bilateral lower extremities. The hospital consult was also obtained to " +\
"address edema issue question was related to his liver hepatitis C. Hospital consult" +\
" was obtained. This included an ultrasound of his abdomen, which showed just mild " +\
"cirrhosis. "
rush = RuSH('../conf/rush_rules.tsv')
sentences=rush.segToSentenceSpans(input_str)
for sentence in sentences:
print('Sentence({0}-{1}):\t>{2}<'.format(sentence.begin, sentence.end, input_str[sentence.begin:sentence.end]))
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