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Explainable and externally validated machine learning for neurocognitive diagnosis via ECGs
Juan Miguel Lopez Alcaraz, Ebenezer Oloyede, David Taylor, Wilhelm Haverkamp, Nils Strodthoff. General Psychiatry, 2025-10-01
Topic: Risk, prevention and care in population studies
Significant predictive performance was observed for several neurocognitive disorders. The highest predictive performance was observed for F03: dementia, with an internal AUROC of 0.848 (95% confidence interval (CI) 0.848 to 0.848) and an external AUROC of 0.865 (95% CI 0.864 to 0.965), followed by G30: Alzheimer's disease, with an internal AUROC of 0.809 (95% CI 0.808 to 0.810) and an external AUROC of 0.863 (95% CI 0.863 to 0.864). Feature importance analysis revealed both established and novel ECG correlates. These findings suggest that ECG holds promise as a non-invasive, explainable biomarker for selected neurocognitive disorders. This study demonstrates robust performance across cohorts and lays the groundwork for future clinical applications, including early detection and personalised monitoring.
Key findings
- Significant predictive performance was observed for several neurocognitive disorders.
- The highest predictive performance was observed for F03: dementia, with an internal AUROC of 0.848 (95% confidence interval (CI) 0.848 to 0.848) and an external AUROC of 0.865 (95% CI 0.864 to 0.965), followed by G30: Alzheimer's disease, with an internal AUROC of 0.809 (95% CI 0.808 to 0.810) and an external AUROC of 0.863 (95% CI 0.863 to 0.864).
- Feature importance analysis revealed both established and novel ECG correlates.
- These findings suggest that ECG holds promise as a non-invasive, explainable biomarker for selected neurocognitive disorders.
- This study demonstrates robust performance across cohorts and lays the groundwork for future clinical applications, including early detection and personalised monitoring.
Why it matters
Machine learning applied to electrocardiograms showed significant predictive performance for dementia and Alzheimer's disease, with external AUROC values of 0.865 and 0.863 respectively. These findings suggest ECG holds promise as a non-invasive biomarker for early detection and monitoring, but this does not treat, cure, or prevent Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 37/100
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 (General Psychiatry) |
| Journal's citation record | 0 / 4 | General Psychiatry: journal h-index 50 (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 | 4 / 5 | 2025 — within 3 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 | 1 / 7 | Cited 3 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Nils Strodthoff: h-index 30 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Oxford: institution h-index 2,205 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41158696) |
| Free to read | 2 / 2 | Free to read |
| Total | 37 / 100 | — |
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Read the original
- DOI: 10.1136/gpsych-2025-102107
- PubMed: 41158696
- Original source →
- Free full text: General Psychiatry →
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. Check the citation against the original before you use it.
APA 7th
Alcaraz, J. M. L., Oloyede, E., Taylor, D., Haverkamp, W., & Strodthoff, N. (2025). Explainable and externally validated machine learning for neurocognitive diagnosis via ECGs. General Psychiatry. https://doi.org/10.1136/gpsych-2025-102107
Vancouver
Alcaraz JML, Oloyede E, Taylor D, Haverkamp W, Strodthoff N. Explainable and externally validated machine learning for neurocognitive diagnosis via ECGs. General Psychiatry. 2025. doi:10.1136/gpsych-2025-102107. PMID: 41158696.
BibTeX
@article{alcaraz2025-41158696,
author = {Alcaraz, Juan Miguel Lopez and Oloyede, Ebenezer and Taylor, David and Haverkamp, Wilhelm and Strodthoff, Nils},
title = {{Explainable and externally validated machine learning for neurocognitive diagnosis via ECGs}},
journal = {General Psychiatry},
year = {2025},
doi = {10.1136/gpsych-2025-102107},
pmid = {41158696},
url = {https://doi.org/10.1136/gpsych-2025-102107}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.