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A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures
Grassi M, Rouleaux N, Caldirola D, Loewenstein D, Schruers K, Perna G, Dumontier M, Alzheimer's Disease Neuroimaging Initiative.. Frontiers in neurology, 2019-07-16
Topic: Amyloid, tau and blood-test diagnosis
The final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data. The specificity of the algorithm was 40.2% for 100% sensitivity. The algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice. This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.
Key findings
- The final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data.
- The specificity of the algorithm was 40.2% for 100% sensitivity.
- The algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice.
- This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.
Why it matters
This study developed a machine learning algorithm using easily obtained clinical information to predict conversion from mild cognitive impairment to Alzheimer's disease. The authors suggest it could help improve recruitment in clinical trials and more selectively prescribe early interventions to high-risk patients, but it does not itself test a treatment, cure or prevention.
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Study details
- Effect size77.7%; 79.9%; 40.2%
- Evidence strengthLow
How this study scored 51/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 49 of the 96 points we could judge it on, which is 51 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in neurology) |
| Journal's citation record | 2 / 4 | Frontiers in Neurology: journal h-index 162 (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 | 2 / 5 | 2019 — within 10 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 125 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 13.9 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 4 / 6 | Cited 8.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 10 other studies in this library |
| Still being cited | 3 / 4 | Cited 51 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 1 / 3 | University of Miami: institution h-index 998 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31379711) |
| Free to read | 2 / 2 | Free to read |
| Points | 49 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 49 of the 96 points we could judge it on is 51 out of 100. |
| Total | 51 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fneur.2019.00756
- PubMed: 31379711
- Original source →
- Free full text: Frontiers in Neurology →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Grassi, M., Rouleaux, N., Caldirola, D., Loewenstein, D., Schruers, K., Perna, G., et al. (2019). A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures. Frontiers in neurology. https://doi.org/10.3389/fneur.2019.00756
Vancouver
Grassi M, Rouleaux N, Caldirola D, Loewenstein D, Schruers K, Perna G, et al. A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures. Frontiers in neurology. 2019. doi:10.3389/fneur.2019.00756. PMID: 31379711.
BibTeX
@article{grassi2019-31379711,
author = {Grassi, M. and Rouleaux, N. and Caldirola, D. and Loewenstein, D. and Schruers, K. and Perna, G. and Dumontier, M. and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures}},
journal = {Frontiers in neurology},
year = {2019},
doi = {10.3389/fneur.2019.00756},
pmid = {31379711},
url = {https://doi.org/10.3389/fneur.2019.00756}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 19.
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