| アイテムタイプ |
学術雑誌論文 = Journal Article(1) |
| 公開日 |
2024-11-06 |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| タイトル |
|
|
タイトル |
A Smart Multimodal Biomedical Diagnosis Based on Patient’s Medical Questions and Symptoms |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 著者 |
Gunturu, Vijaya
Krishnamoorthy, R.
Amina Begum, M.
Jayakarthik, R.
田中, 和明
Ramesh, Janjhyam Venkata Naga
|
| 抄録 |
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|
内容記述タイプ |
Abstract |
|
内容記述 |
The exponential increase of health-related digital data has given machine learning algorithms a newfound ability to generate more meaningful insights. Information such as diagnosis, treatments, and prescriptions are all part of digital health data. In order to better care for their patients, healthcare providers provide crucial diagnostic services. Mistakes in diagnosis, however, lead to the patient receiving harmful treatment too soon or too late. In order to reduce the likelihood of clinical cognitive errors, computer-aided diagnosis techniques have been developed. The proposed approach utilizes a massive health-related data set, which is comprised of many unstructured patient questions written in various Arabic dialects, as well as symptoms reported by general practitioners (GPs). System components include a combination of machine learning models that have been trained using either patient symptoms or patient medical inquiries. Machine learning (ML) strategies, and variations of the multilayer perceptron (MLP) classifier have all been utilized in trials as feature representation techniques and ML classifiers. We also discuss the technical and analytical hurdles, as well as the most important new applications, that this research opens up. Possibilities in areas such as digital clinical trials, telehealth, pandemic surveillance, digital twins, and virtual health aides are discussed. We also provide an overview of the data, modeling, and privacy obstacles that must be surmounted before the healthcare industry can fully benefit from multimodal AI. With a classification accuracy of 94.9%, the combined results of the two modalities demonstrate promising prediction potential. The results show promise for using the algorithm to predict possible diagnoses of patient illnesses that depend on the given symptoms and queries, which can help doctors make more informed judgments. |
|
言語 |
en |
| 書誌情報 |
en : 5G-Based Smart Hospitals and Healthcare Systems
発行日 2023-12-13
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| 出版社 |
|
|
出版者 |
Taylor & Francis |
|
言語 |
en |
| DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1201/9781003403678-8 |
| ISBN |
|
|
|
識別子タイプ |
ISBN |
|
|
関連識別子 |
9781003403678 |
| 著作権関連情報 |
|
|
権利情報 |
This is an Accepted Manuscript of an article published by Taylor & Francis in 5G-Based Smart Hospitals and Healthcare Systems on 13 December, 2023, available online: http://www.tandfonline.com/10.1201/9781003403678. |
| 出版タイプ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 査読の有無 |
|
|
値 |
yes |
| 研究者情報 |
|
|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/251_ja.html |
| 論文ID（連携） |
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|
値 |
10441659 |
| 連携ID |
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|
値 |
12379 |
10441659.pdf (882.4 KB)
