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Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review
Kumar S, Oh I, Schindler S, Lai AM, Payne PRO, Gupta A.. JAMIA open, 2021-07-01
Topic: Amyloid, tau and blood-test diagnosis
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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APA 7th
Kumar, S., Oh, I., Schindler, S., Lai, A. M., Payne, P. R. O., & Gupta, A. (2021). Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review. JAMIA open. https://doi.org/10.1093/jamiaopen/ooab052
Vancouver
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. doi:10.1093/jamiaopen/ooab052. PMID: 34350389.
BibTeX
@article{kumar2021-34350389,
author = {Kumar, S. and Oh, I. and Schindler, S. and Lai, A. M. and Payne, P. R. O. and Gupta, A.},
title = {{Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review}},
journal = {JAMIA open},
year = {2021},
doi = {10.1093/jamiaopen/ooab052},
pmid = {34350389},
url = {https://doi.org/10.1093/jamiaopen/ooab052}
}
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Key findings
- 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.
- 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).
- Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.
Why it matters
This systematic review found a considerable rise in research using machine learning on clinical data to model risk for progression of Alzheimer's disease dementia. It is a review of prediction methods and does not test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 44/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (JAMIA open) |
| Journal's citation record | 0 / 4 | JAMIA Open: journal h-index 49 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2021 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 122 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 17.4 citations a year over 7 year(s) since 2021 |
| Impact for its field and year | 3 / 6 | Cited 4.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 6 other studies in this library |
| Still being cited | 3 / 4 | Cited 75 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Aditi Gupta: h-index 13 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Washington University in St. Louis: institution h-index 1,962 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 34350389) |
| Free to read | 2 / 2 | Free to read |
| Total | 44 / 100 | — |
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