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Comorbidities of dementia in electronic health records: validation of a natural language processing app and comparison with data linkage
Mirza L, Bendayan R, Stewart R, Davis KA.. medRxiv, 2026-10-19
Topic: Health services, long-term care and technology
MedCAT Comorbidity demonstrated predictive ability for mortality (HR 1.11 per condition), although the model fit was better using comorbidity from hospital admission data. Results from the MedCAT comorbidity app can assist researchers in understanding comorbidity of cohorts, particularly when primary care or hospital data is not available. Our findings highlight the need to develop quality assurance protocols that include internal and external validity checks before deploying general data extraction apps for specific clinical subpopulations. The results indicated some potential strengths and limitations of the NLP approach, which may vary by comorbidity condition.
Key findings
- MedCAT Comorbidity demonstrated predictive ability for mortality (HR 1.11 per condition), although the model fit was better using comorbidity from hospital admission data.
- Results from the MedCAT comorbidity app can assist researchers in understanding comorbidity of cohorts, particularly when primary care or hospital data is not available.
- Our findings highlight the need to develop quality assurance protocols that include internal and external validity checks before deploying general data extraction apps for specific clinical subpopulations.
- The results indicated some potential strengths and limitations of the NLP approach, which may vary by comorbidity condition.
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Study details
- Participants3,543
- Effect sizeHR 1.11
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 39 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 24 of the 62 points we could judge it on, which is 39 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 3 / 4 | 3,543 participants |
| Effect reported | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 24 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 24 of the 62 points we could judge it on is 39 out of 100. |
| Total | 39 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
Read the original
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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
Mirza, L., Bendayan, R., Stewart, R., & Davis, K. A. (2026). Comorbidities of dementia in electronic health records: validation of a natural language processing app and comparison with data linkage. medRxiv. https://doi.org/10.64898/2026.10.16.26365714
Vancouver
Mirza L, Bendayan R, Stewart R, Davis KA. Comorbidities of dementia in electronic health records: validation of a natural language processing app and comparison with data linkage. medRxiv. 2026. doi:10.64898/2026.10.16.26365714.
BibTeX
@article{mirza2026-c75ed62e,
author = {Mirza, L. and Bendayan, R. and Stewart, R. and Davis, K. A.},
title = {{Comorbidities of dementia in electronic health records: validation of a natural language processing app and comparison with data linkage}},
journal = {medRxiv},
year = {2026},
doi = {10.64898/2026.10.16.26365714},
url = {https://doi.org/10.64898/2026.10.16.26365714}
}
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