Continuous health care evaluating for acute ischemic stroke patients with significant factor neural network relapse prediction model.
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| Title: | Continuous health care evaluating for acute ischemic stroke patients with significant factor neural network relapse prediction model. |
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| Authors: | Yu, Lili1 (AUTHOR), Kong, Zhaoli2 (AUTHOR), Zhao, Youwei3 (AUTHOR) youwei@hainanu.edu.cn |
| Source: | Technology & Health Care. Mar2026, Vol. 34 Issue 2, p229-245. 17p. |
| Subjects: | Continuum of care, Disease relapse, Health services administration, Artificial neural networks, Psychological stress, Ischemic stroke, Therapeutics, Chronic diseases |
| Abstract: | The effect of continuous medical service intervention on health management for people who have suffered from Acute Ischemic Stroke (AIS) is an important issue in health care tracking. To pick out core aspects related to health, a relapse prediction model, evaluate the efficiency of continuous care and boost post-discharge results, a structured study is designed. After investigation and scientific verification, important signs and symptoms were chosen to set up a Significant Factors Neural Network Relapse Prediction Model (SFNNR) which aims to predict possible relapses based on previous patterns in medical data. The continuous care group was compared with the control group, and it turned out that participants in continuous care had significantly better results with fewer chances of having relapses and controlling chronic risks while displaying less psychological stress compared to the control group; furthermore, the continuous medical service showed great value on long-term management of AIS patients. The study points out that the integrated care approach should be taken more seriously as it can help healthcare staff predict the risk of relapse accurately so as to come up with personalized plans to control the relapse probability of the patients. [ABSTRACT FROM AUTHOR] |
| Copyright of Technology & Health Care is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 192982046 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Continuous health care evaluating for acute ischemic stroke patients with significant factor neural network relapse prediction model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu%2C+Lili%22">Yu, Lili</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kong%2C+Zhaoli%22">Kong, Zhaoli</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Youwei%22">Zhao, Youwei</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> youwei@hainanu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technology+%26+Health+Care%22">Technology & Health Care</searchLink>. Mar2026, Vol. 34 Issue 2, p229-245. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Continuum+of+care%22">Continuum of care</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+relapse%22">Disease relapse</searchLink><br /><searchLink fieldCode="DE" term="%22Health+services+administration%22">Health services administration</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+stress%22">Psychological stress</searchLink><br /><searchLink fieldCode="DE" term="%22Ischemic+stroke%22">Ischemic stroke</searchLink><br /><searchLink fieldCode="DE" term="%22Therapeutics%22">Therapeutics</searchLink><br /><searchLink fieldCode="DE" term="%22Chronic+diseases%22">Chronic diseases</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The effect of continuous medical service intervention on health management for people who have suffered from Acute Ischemic Stroke (AIS) is an important issue in health care tracking. To pick out core aspects related to health, a relapse prediction model, evaluate the efficiency of continuous care and boost post-discharge results, a structured study is designed. After investigation and scientific verification, important signs and symptoms were chosen to set up a Significant Factors Neural Network Relapse Prediction Model (SFNNR) which aims to predict possible relapses based on previous patterns in medical data. The continuous care group was compared with the control group, and it turned out that participants in continuous care had significantly better results with fewer chances of having relapses and controlling chronic risks while displaying less psychological stress compared to the control group; furthermore, the continuous medical service showed great value on long-term management of AIS patients. The study points out that the integrated care approach should be taken more seriously as it can help healthcare staff predict the risk of relapse accurately so as to come up with personalized plans to control the relapse probability of the patients. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technology & Health Care is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/09287329251392397 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 229 Subjects: – SubjectFull: Continuum of care Type: general – SubjectFull: Disease relapse Type: general – SubjectFull: Health services administration Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Psychological stress Type: general – SubjectFull: Ischemic stroke Type: general – SubjectFull: Therapeutics Type: general – SubjectFull: Chronic diseases Type: general Titles: – TitleFull: Continuous health care evaluating for acute ischemic stroke patients with significant factor neural network relapse prediction model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu, Lili – PersonEntity: Name: NameFull: Kong, Zhaoli – PersonEntity: Name: NameFull: Zhao, Youwei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09287329 Numbering: – Type: volume Value: 34 – Type: issue Value: 2 Titles: – TitleFull: Technology & Health Care Type: main |
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