Evidence map›Paper›PMID 41639879›Full record

ArticleOrphanet journal of rare diseases2026

Evaluating algorithmic approaches to rare disease case-finding: a retrospective validation study using electronic health records.

Freya Boardman-Pretty, Jyothika Kumar, Calum Grant, Elena Marchini, William Evans, Lara Menzies, Rand Dubis, Amanda Worker, Elizabeth Varones, Alan Warren and 5 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Orphanet journal of rare diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Freya Boardman-PrettyMendelian, The Trampery, Old Street, London, UK. freya@mendelian.co.ORCID http://orcid.org/0000-0001-8459-4739
Jyothika KumarMendelian, The Trampery, Old Street, London, UK.
Calum GrantMendelian, The Trampery, Old Street, London, UK.
Elena MarchiniMendelian, The Trampery, Old Street, London, UK.
William EvansMendelian, The Trampery, Old Street, London, UK.
Lara MenziesMendelian, The Trampery, Old Street, London, UK.
Rand DubisMendelian, The Trampery, Old Street, London, UK.
Amanda WorkerMendelian, The Trampery, Old Street, London, UK.
Elizabeth VaronesMendelian, The Trampery, Old Street, London, UK.
Alan WarrenMendelian, The Trampery, Old Street, London, UK.
Jack SamsMendelian, The Trampery, Old Street, London, UK.
Daniel OllerenshawMendelian, The Trampery, Old Street, London, UK.
Jez StockdaleMendelian, The Trampery, Old Street, London, UK.
Hadley MahonMendelian, The Trampery, Old Street, London, UK.
Peter FishMendelian, The Trampery, Old Street, London, UK.

Funding

Department of Health and Social Care AI_AWARD02579
6 · The paper itself

Abstract

backgroundAccurate and early diagnosis to optimise rare disease care is a global priority. With recent developments in artificial intelligence (AI)-based solutions, a promising area to improve rare disease diagnosis is the application of AI to routinely collected health care data captured in electronic health records (EHRs). MendelScan is a rare disease case-finding tool that analyses structured EHR data, using algorithms to identify patterns that are associated with an increased likelihood of the patient being affected by one of a number of rare diseases, in order to put such patients forward for further review.

methodsIn this paper, we evaluated the performance of case-finding algorithms for 34 rare diseases within MendelScan, by performing a retrospective validation study using research EHR data. The primary objectives were to assess MendelScan’s ability to correctly identify cases versus controls, and to investigate other metrics indicating feasibility of large-scale deployment and time identified (flagged) relative to diagnosis. We measured algorithm performance by sensitivity, specificity, positive predictive value (PPV), and likelihood ratios.

resultsAlgorithm performance varied from metric to metric for the different algorithms. Sensitivity ranged from 0 to 100%, but majority were under 25% (median = 3.8% (IQR: 1.2–12.6%)) whereas specificity for most algorithms was above 99.995% (median = 99.9966% (IQR: 99.9925–99.9988)). Median PPV adjusted by literature prevalence was 3.1% (IQR: 0.7–14.6%) and by coded prevalence, 2.5% (IQR: 0.4–8.4). Median positive likelihood ratio was 1167 (IQR: 125–4006), reflecting a strong signal for disease presence, and median negative likelihood ratio was 0.96 (IQR: 0.87–0.99), reflecting limited clinical utility of a negative result.

conclusionsOur findings demonstrate the potential of using routinely collected EHR data to facilitate earlier diagnosis of rare diseases. Real world evaluations are required in order to fully ascertain the impact of such case-finding algorithms in assisting with the detection and diagnosis of patients with rare diseases.

Indexed as

AlgorithmsElectronic Health RecordsRare DiseasesArtificial IntelligenceHumansRetrospective StudiesAlgorithmCase-findingDiagnosisEHRRare diseaseValidation

Identifiers

PMID41639879
PMCPMC13041464

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