PyRuSH[jre] is the python interface to RuSH(https://github.com/jianlins/RuSH) (Ru le-based sentence S egmenter using H ashing), which is originally developed using Java. This jre branch is implemented through py4j, compared with default PyRuSH.

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 here.

pip install PyRuSH[jre]

A standalone RuSH class is available to be directly used in your code. From 1.0.4, pyRush adopt spaCy 3.x api to initiate an component.

from PyRuSH 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]))

Start from version 1.0.3, PyRuSH adds Spacy compatible Sentencizer component: PyRuSHSentencizer.

from PyRuSH import PyRuSHSentencizer
from spacy.lang.en
import English \>\>\> nlp = English() \>\>\>
nlp.add_pipe("medspacy_pyrush")
doc = nlp("This is a sentence. This is another sentence.")
print('n'.join(\[str(s) for s in doc.sents\]))

Feel free to try this runnable Colab notebook Demo