Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke.
Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is wid...
| Publicado en: | Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 21 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
1/2/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174877257&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174877257 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 1/2/2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 174877257 174877257 174877257 10.1007/s10916-023-02020-4 174877257 ppf: 1 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke. aug: au: Chen, Meng Qian, Dongbao Wang, Yixuan An, Junyan Meng, Ke Xu, Shuai Liu, Sheng Sun, Meiyan Li, Miao Pang, Chunying affil: https://ror.org/007mntk44 School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, 130022, Changchun, People's Republic of China sug: subj: Machine Learning Algorithms Ischemic Stroke Prognosis Cerebral Ischemia, Transient Prognosis Risk Assessment Ischemic Stroke Prevention and Control Cerebral Ischemia, Transient Prevention and Control Human Systematic Review Prediction Models Cardiovascular Diseases Complications Age Factors Sex Factors NIH Stroke Scale Diabetes Mellitus Complications Descriptive Statistics PubMed Individualized Medicine Ischemic Stroke Therapy Cerebral Ischemia, Transient Therapy Funding Source ab: Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is widely used for the prediction of ischemic stroke outcomes. By identifying risk factors, predicting the risk of poor prognosis and thus developing personalized treatment plans, it effectively reduces the probability of poor prognosis, leading to more effective secondary prevention. This review includes 41 studies since 2018 that used ML algorithms to build prognostic prediction models for ischemic stroke, transient ischemic attack (TIA), and acute ischemic stroke (AIS). We analyzed in detail the risk factors used in these studies, the sources and processing methods of the required data, the model building and validation, and their application in different prediction time windows. The results indicate that among the included studies, the top five risk factors in terms of frequency were cardiovascular diseases, age, sex, national institutes of health stroke scale (NIHSS) score, and diabetes. Furthermore, 64% of the studies used single-center data, 65% of studies using imbalanced data did not perform data balancing, 88% of the studies did not utilize external validation datasets for model validation, and 72% of the studies did not provide explanations for their models. Addressing these issues is crucial for enhancing the credibility and effectiveness of the research, consequently improving the development and implementation of secondary prevention measures. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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