Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.
Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the dise...
| Publicado en: | Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 26 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
2/1/2023
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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=179649622&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179649622 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2/1/2023 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179649622 179649622 179649622 10.1007/s10916-023-01906-7 179649622 ppf: 1 ppct: 25 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. aug: au: Javeed, Ashir Dallora, Ana Luiza Berglund, Johan Sanmartin Ali, Arif Ali, Liaqat Anderberg, Peter affil: https://ror.org/056d84691 Aging Research Center, Karolinska Institutet, Tomtebodavagen, 17165, Stockholm, Solna, Sweden sug: subj: Machine Learning Automation Diagnosis, Computer Assisted Dementia Diagnosis Diagnostic Imaging Dementia Symptoms Disease Attributes Human Systematic Review Deep Learning Early Diagnosis Dementia Classification Disease Progression Aging PubMed Descriptive Statistics ab: Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations. pubtype: Academic Journal doctype: equations & formulas pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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