To Identify Disease Treatment Relationship in Short Text Using Machine Learning & Natural Language Processing
DOI:
https://doi.org/10.61841/kvchxv90Keywords:
Machine Learning, Victimization, medical papersAbstract
Due to advancements in medical domain automatic learning has gained quality within the fields of medical call support, extraction of medical data and complete health management. Victimization Machine Learning (hereafter, ML) and language process (hereafter, NLP) we are able to create the tending field a lot of economical and reliable. This paper describes however millilitre and informatics will be used for extracting data from printed medical papers. It extracts the sentences that mention diseases and coverings and identifies the link between them. This technology once place into significant U.S.A.e leads us to discovery of a lot of data regarding ourselves, our environments and conjointly the devices we tend to use. This will be achieved through assortment of knowledge of activities through devices like good phones and different personal devices. This work is intended by the requirement to erase or a minimum of lower the complexness and difficulties close integration of knowledge nonheritable from completely different domains. This will be additional wont to enhance information capturing, protective privileged info and its sources, search, sharing, analytics and so enhance its use for additional analysis and studies. The emergence of huge information could be a results of living in societies that makes increasing use of knowledge intensive technologies.
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References
[1] Oana Frunza, Diana Inkpen, and Thomas Tran " A Machine Learning Approach for Identifying Disease-Treatment Relations in Short Texts" vol. 23, 2011.
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[8] http://nlp.stanford.edu/software/tagger.sht ml.
[9] Google health report, https://www.google.com/health [10]Microsoft Health Vault, http://healthvault.com
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