PyRuSH is the python implementation of RuSH (Ru le-based sentence S egmenter using H ashing), which is originally 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 here.
pip install PyRuSH
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 03/26/08n and was started on IV antibiotics elevation" +>>> ", was also counseled to minimizing the cigarette smoking. The patient had edemann" +>>> "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