Title | A Conceptual Framework to Predict Mental Health Patients' Zoning Classification. |
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Type | Journal article |
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Authors | Pandey, Sanjib Raj, Smith, Alan, Gall, Edmund Nigel, Bhatnagar, Ajay and Chaussalet, Thierry |
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Abstract | Zoning classification is a rating mechanism, which uses a three-tier color coding to indicate perceived risk from the patients' conditions. It is a widely adopted manual system used across mental health settings, however it is time consuming and costly. We propose to automate classification, by adopting a hybrid approach, which combines Temporal Abstraction to capture the temporal relationship between symptoms and patients' behaviors, Natural Language Processing to quantify statistical information from patient notes, and Supervised Machine Learning Models to make a final prediction of zoning classification for mental health patients. |
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Keywords | Supervised Machine Learning |
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| zoning |
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| Machine Learning |
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| Natural Language Processing |
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| mental health |
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| Humans |
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| Mental Health |
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| machine learning |
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| natural language processing |
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| temporal logic |
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| Electronic Health Records |
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Journal | Studies in Health Technology and Informatics |
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Journal citation | 289, pp. 321-324 |
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ISSN | 1879-8365 |
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Year | 2022 |
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Publisher | IOS Press |
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Publisher's version | License CC BY-NC 4.0 File Access Level Open (open metadata and files) |
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Digital Object Identifier (DOI) | https://doi.org/10.3233/SHTI210924 |
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PubMed ID | 35062157 |
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Publication dates |
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Published | 14 Jan 2022 |
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Published in print | 14 Jan 2022 |
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Published | 14 Jan 2022 |
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Book title | Volume 289: Informatics and Technology in Clinical Care and Public Health |
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Page range | 321-324 |
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ISBN | 9781643682501 |
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| 9781643682518 |
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Editors | Mantas, J., Hasman, A., Househ, M.S., Gallos, P., Zoulias, E. and Liaskos, J. |
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File | License CC BY-NC 4.0 File Access Level Open (open metadata and files) |
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