ArticleMed (New York, N.Y.)2026
Capturing multi-disease states on a spectrum with machine learning and routine clinical data.
Article in Med (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
backgroundDiseases exist on spectra of risk factors, cellular perturbations, organ dysfunction, and clinical manifestations. It is unknown whether the analysis of routine laboratory tests and vitals using artificial intelligence presents a scalable and portable system for capturing the spectral nature of common diseases.
methodsWe constructed and validated machine learning models targeting seven common diseases-atrial fibrillation, breast cancer, coronary artery disease, migraine, rheumatoid arthritis, schizophrenia, and type 2 diabetes-using routine clinical measurements from 394,957 electronic health records (EHRs) in the BioMe Biobank and UK Biobank. The Resulting model outputs, termed spectral health index from machine measurements of electronic records (SHIMMER), were assessed for association with disease diagnosis, risk factors, biomarkers, onset, survival, complications, and medications in two cohorts.
findingsSHIMMER was associated with disease diagnosis, known risk factors, and biomarkers in expected directions in both cohorts. With greater SHIMMER, the prevalence of risk factors, complications, and medications continuously increased; for instance, age and hypertension, stroke risk and cardiac arrest, and beta blockers increased, respectively, with atrial fibrillation SHIMMER. Biomarker levels for type 2 diabetes, such as glucose, hemoglobin A1c, C-reactive protein, and triglycerides, changed stepwise as SHIMMER increased. Rising SHIMMER also revealed gradations of earlier disease onset and decreased survival, particularly for coronary artery disease and schizophrenia.
conclusionsA holistic, non-invasive marker derived from machine learning trained on routine clinical measurements snapshots multiple common diseases on a spectrum, quantifying disease risk, severity, onset, survival, sequela, and treatment.
fundingThis study was supported in part by the National Institutes of Health.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.