Abstract
The significance of integrating Natural Language Processing (NLP) approaches in healthcare research has become more prominent in recent years, and it has had a transformational impact on the state-of-the-art. In healthcare, NLPs are developed as well as assessed on the basis of words, phrases, or record-level explanations based on patient reports such as side-effects of medications, Medicines for illnesses or semantic characteristics are prescribed (nullification, seriousness), etc. While some NLP projects take into account customer expectations at the level of an individual or a group, these projects are still in the minority. A special focus is placed on psychological wellness research, which is currently the subject of little research in healthcare NLP research networks but where NLP approaches are widely used. Although there have been significant advancements in healthcare NLP strategy improvement, we believe that in order for the profession to grow further, more emphasis should be placed on comprehensive evaluation. To help with this, we offer some helpful ideas, including one on a minor etiquette that may be used when announcing clinical NLP strategy improvement and assessment.
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Choudhary, A., Choudhary, A., Suman, S. (2022). NLP Applications for Big Data Analytics Within Healthcare. In: Mishra, S., Tripathy, H.K., Mallick, P., Shaalan, K. (eds) Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis. Studies in Computational Intelligence, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-19-1076-0_13
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