State-of-the-art methods in healthcare text classification syste AI paradigm
Machine learning has shown its importance in delivering healthcare solutions and revolutionizing the future of filtering huge amountd of textual content. The machine intelligence can adapt semantic relations among text to infer finer contextual information and language processing system can use this...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IMR Press
2020-01-01
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| Series: | Frontiers in Bioscience-Landmark |
| Subjects: | |
| Online Access: | https://www.imrpress.com/journal/FBL/25/4/10.2741/4826 |
| _version_ | 1828283005756506112 |
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| author | Saurabh Kumar Srivastava Sandeep Kumar Singh Jasjit S. Suri |
| author_facet | Saurabh Kumar Srivastava Sandeep Kumar Singh Jasjit S. Suri |
| author_sort | Saurabh Kumar Srivastava |
| collection | DOAJ |
| description | Machine learning has shown its importance in delivering healthcare solutions and revolutionizing the future of filtering huge amountd of textual content. The machine intelligence can adapt semantic relations among text to infer finer contextual information and language processing system can use this information for better decision support and quality of life care. Further, a learnt model can efficiently utilize written healthcare information in knowledgeable patterns. The word–document and document–document linkage can help in gaining better contextual information. We analyzed 124 research articles in text and healthcare domain related to the ML paradigm and showed the mechanism of intelligence to capture hidden insights from document representation where only a term or word is used to explain the phenomenon. Mostly in the research, document–word relations are identified while relations with other documents are ignored. This paper emphasizes text representations and its linage with ML, DL, and RL approaches, which is an important marker for intelligence segregation. Furthermore, we highlighted the advantages of ML and DL methods as powerful tools for automatic text classification tasks. |
| first_indexed | 2024-04-13T08:38:12Z |
| format | Article |
| id | doaj.art-b85775dd7f06492fa603401480d75f55 |
| institution | Directory Open Access Journal |
| issn | 2768-6701 |
| language | English |
| last_indexed | 2024-04-13T08:38:12Z |
| publishDate | 2020-01-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Frontiers in Bioscience-Landmark |
| spelling | doaj.art-b85775dd7f06492fa603401480d75f552022-12-22T02:54:01ZengIMR PressFrontiers in Bioscience-Landmark2768-67012020-01-0125464667210.2741/4826FBS-25-643State-of-the-art methods in healthcare text classification syste AI paradigmSaurabh Kumar Srivastava0Sandeep Kumar Singh1Jasjit S. Suri2Department of CSE, ABES Engineering College Ghaziabad, IndiaDepartment of CSE, JIIT University, Noida, IndiaAdvanced Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA, USAMachine learning has shown its importance in delivering healthcare solutions and revolutionizing the future of filtering huge amountd of textual content. The machine intelligence can adapt semantic relations among text to infer finer contextual information and language processing system can use this information for better decision support and quality of life care. Further, a learnt model can efficiently utilize written healthcare information in knowledgeable patterns. The word–document and document–document linkage can help in gaining better contextual information. We analyzed 124 research articles in text and healthcare domain related to the ML paradigm and showed the mechanism of intelligence to capture hidden insights from document representation where only a term or word is used to explain the phenomenon. Mostly in the research, document–word relations are identified while relations with other documents are ignored. This paper emphasizes text representations and its linage with ML, DL, and RL approaches, which is an important marker for intelligence segregation. Furthermore, we highlighted the advantages of ML and DL methods as powerful tools for automatic text classification tasks.https://www.imrpress.com/journal/FBL/25/4/10.2741/4826text classificationdocumentscorpussocial mediainput text characterizationartificial intelligence |
| spellingShingle | Saurabh Kumar Srivastava Sandeep Kumar Singh Jasjit S. Suri State-of-the-art methods in healthcare text classification syste AI paradigm Frontiers in Bioscience-Landmark text classification documents corpus social media input text characterization artificial intelligence |
| title | State-of-the-art methods in healthcare text classification syste AI paradigm |
| title_full | State-of-the-art methods in healthcare text classification syste AI paradigm |
| title_fullStr | State-of-the-art methods in healthcare text classification syste AI paradigm |
| title_full_unstemmed | State-of-the-art methods in healthcare text classification syste AI paradigm |
| title_short | State-of-the-art methods in healthcare text classification syste AI paradigm |
| title_sort | state of the art methods in healthcare text classification syste ai paradigm |
| topic | text classification documents corpus social media input text characterization artificial intelligence |
| url | https://www.imrpress.com/journal/FBL/25/4/10.2741/4826 |
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