Bibliographic Details
| Title: |
Precise Thrombolytic Treatment for Stroke Using AI-based Algorithms: a Real-world Study |
| Authors: |
SHEN Huiwen, LIN Yongzhong, CHEN Shuliang, ZHANG Lihong, MA Chunye, MA Deyuan, ZHANG Ce |
| Source: |
Zhongguo quanke yixue, Vol 26, Iss 17, Pp 2070-2077 (2023) |
| Publisher Information: |
Chinese General Practice Publishing House Co., Ltd, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Medicine |
| Subject Terms: |
ischemic stroke, thrombolytic drugs, artificial intelligence algorithm, wide&deep model, precision treatment, Medicine |
| More Details: |
Background The thrombolytic effect for ischemic stroke (IS) is affected by complex factors, such as acute onset of stroke, short therapeutic time window, various individual patient factors, treatment model, types and doses of medicines as well as mode of administration. To identify the influencing factors of thrombolytic effect, most existing studies adopt statistical methods, while rare studies use artificial intelligence (AI) -based algorithms.Objective To establish models using AI-based algorithms for IS patients based on the real-world data including general patient characteristics, medication model and recovery effects, to achieve precise individualized thrombolytic treatment and provide data support for clinical prescription decisions.Methods A retrospective design was used. The clinical information of IS patients (n=55 621) was extracted from the Yidu Cloud scientific research big data server system of the Second Affiliated Hospital of Dalian Medical University from January 1, 2001 to December 31, 2021, among whom 1 855 with complete information were enrolled according to the inclusion criteria. Thrombolysis effect was evaluated by comparing the National Institutes of Health Stroke Scale (NIHSS) score measured at admission and discharge, and those with an improvement in the NIHSS score by ≥4 points and |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
Chinese |
| ISSN: |
1007-9572 |
| Relation: |
https://www.chinagp.net/fileup/1007-9572/PDF/20230048.pdf; https://doaj.org/toc/1007-9572 |
| DOI: |
10.12114/j.issn.1007-9572.2023.0048 |
| Access URL: |
https://doaj.org/article/6be311967d844906b1b4876120974852 |
| Accession Number: |
edsdoj.6be311967d844906b1b4876120974852 |
| Database: |
Directory of Open Access Journals |
