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Machine learning for comprehensive forecasting of Alzheimer's Disease progression
Fisher CK, Smith AM, Walsh JR, Coalition Against Major Diseases, Abbott, Alliance for Aging Research, Alzheimer’s Association, Alzheimer’s Foundation of America et al.. Scientific reports, 2019-09-20
Topic: Amyloid, tau and blood-test diagnosis
Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer's Disease. Here, we use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Synthetic patient data generated by the CRBM accurately reflect the means, standard deviations, and correlations of each variable over time to the extent that synthetic data cannot be distinguished from actual data by a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Fisher, C. K., Smith, A. M., Walsh, JR., Diseases, C. A. M., Abbott, Alliance for Aging Research, et al. (2019). Machine learning for comprehensive forecasting of Alzheimer's Disease progression. Scientific reports. https://doi.org/10.1038/s41598-019-49656-2
Vancouver
Fisher CK, Smith AM, Walsh JR, Diseases CAM, Abbott, Alliance for Aging Research, et al. Machine learning for comprehensive forecasting of Alzheimer's Disease progression. Scientific reports. 2019. doi:10.1038/s41598-019-49656-2. PMID: 31541187.
BibTeX
@article{fisher2019-31541187,
author = {Fisher, C. K. and Smith, A. M. and Walsh, JR. and Diseases, Coalition Against Major and Abbott and {Alliance for Aging Research} and {Alzheimer’s Association} and {Alzheimer’s Foundation of America} and others},
title = {{Machine learning for comprehensive forecasting of Alzheimer's Disease progression}},
journal = {Scientific reports},
year = {2019},
doi = {10.1038/s41598-019-49656-2},
pmid = {31541187},
url = {https://doi.org/10.1038/s41598-019-49656-2}
}
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Key findings
- Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression.
Why it matters
This study used an unsupervised machine learning model to simulate 18-month patient trajectories from 1909 patients with mild cognitive impairment or Alzheimer's disease. The model predicted changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, which may help personalized forecasting of disease progression.
Study details
- Participants1,909
- Evidence strengthLow
How this study scored 57/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (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 | 3 / 4 | 1,909 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 220 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 24.4 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 4 / 6 | Cited 16.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 8 other studies in this library |
| Still being cited | 3 / 4 | Cited 88 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Wayne R. Kubick: h-index 7 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Johns Hopkins University: institution h-index 2,306 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 11 institution(s) in 4 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31541187) |
| Free to read | 2 / 2 | Free to read |
| Total | 57 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41598-019-49656-2
- PubMed: 31541187
- Original source →
- Free full text: Scientific Reports →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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Also in Mild cognitive impairment
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