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Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. J Med Syst 2023; 47:17. [PMID: 36720727 PMCID: PMC9889464 DOI: 10.1007/s10916-023-01906-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/03/2023] [Indexed: 02/02/2023]
Abstract
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.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Tomtebodavagen, Stockholm, 17165, Solna, Sweden
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Johan Sanmartin Berglund
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden.
| | - Arif Ali
- Department of Computer Science, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Liaqata Ali
- Department of Electrical Engineering, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
- School of Health Sciences, University of Skovde, Högskolevägen 1, Skövde, SE-541 28, Skövde, Sweden
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Kumar S, Oh I, Schindler S, Lai AM, Payne PRO, Gupta A. Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review. JAMIA Open 2021; 4:ooab052. [PMID: 34350389 PMCID: PMC8327375 DOI: 10.1093/jamiaopen/ooab052] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/21/2021] [Accepted: 06/30/2021] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. MATERIALS AND METHODS We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. RESULTS There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). DISCUSSION Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.
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Affiliation(s)
- Sayantan Kumar
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Inez Oh
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Suzanne Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Albert M Lai
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Philip R O Payne
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Aditi Gupta
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
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Martí-Juan G, Sanroma-Guell G, Piella G. A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease. Comput Methods Programs Biomed 2020; 189:105348. [PMID: 31995745 DOI: 10.1016/j.cmpb.2020.105348] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 01/10/2020] [Accepted: 01/18/2020] [Indexed: 05/02/2023]
Abstract
BACKGROUND AND OBJECTIVES Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems. In this paper, we review recent statistical and machine learning applications in Alzheimer's disease using longitudinal neuroimaging. METHODS We search for papers using longitudinal imaging data, focused on Alzheimer's Disease and published between 2007 and 2019 on four different search engines. RESULTS After the search, we obtain 104 relevant papers. We analyze their approach to typical challenges in longitudinal data analysis, such as missing data and variability in the number and extent of acquisitions. CONCLUSIONS Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis. We compare results and models, and propose future research directions in the field.
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Affiliation(s)
- Gerard Martí-Juan
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | | | - Gemma Piella
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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Graham SA, Lee EE, Jeste DV, Van Patten R, Twamley EW, Nebeker C, Yamada Y, Kim HC, Depp CA. Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review. Psychiatry Res 2020; 284:112732. [PMID: 31978628 PMCID: PMC7081667 DOI: 10.1016/j.psychres.2019.112732] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 12/04/2019] [Accepted: 12/07/2019] [Indexed: 12/13/2022]
Abstract
Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders.
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Affiliation(s)
- Sarah A Graham
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States
| | - Ellen E Lee
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States
| | - Dilip V Jeste
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; Department of Neurosciences, University of California San Diego, La Jolla, CA, United States.
| | - Ryan Van Patten
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States
| | - Elizabeth W Twamley
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States
| | - Camille Nebeker
- Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, United States
| | | | - Ho-Cheol Kim
- IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; Scalable Knowledge Intelligence, IBM Research-Almaden, San Jose, CA, United States
| | - Colin A Depp
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States; IBM-UCSD Artificial Intelligence for Healthy Living Program, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States
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