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Capturing multi-disease states on a spectrum with machine learning and routine clinical data.
Iain S Forrest1, Ben O Petrazzini2, Robert Chen1
1The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Med (New York, N.Y.)
|May 28, 2026
Summary
Artificial intelligence models analyzing routine clinical data create a spectral health index (SHIMMER). This novel marker quantifies disease risk, severity, and outcomes across common conditions.
Area of Science:
- Biomedical Informatics
- Artificial Intelligence in Medicine
- Computational Biology
Background:
- Diseases manifest across a spectrum of risk factors, cellular changes, organ dysfunction, and clinical symptoms.
- The potential of artificial intelligence (AI) to analyze routine laboratory tests and vital signs for capturing disease spectra remains unexplored.
Purpose of the Study:
- To develop and validate AI-driven models for assessing common diseases on a spectrum.
- To determine if routine clinical measurements can be used to create a scalable and portable system for disease spectrum analysis.
Main Methods:
- Machine learning models were constructed and validated for seven common diseases: atrial fibrillation, breast cancer, coronary artery disease, migraine, rheumatoid arthritis, schizophrenia, and type 2 diabetes.
- Models utilized routine clinical measurements from 394,957 electronic health records (EHRs) in the BioMe Biobank and UK Biobank.
- Model outputs, termed spectral health index from machine measurements of electronic records (SHIMMER), were assessed for associations with disease diagnosis, risk factors, biomarkers, onset, survival, complications, and medications.
Main Results:
- SHIMMER demonstrated associations with disease diagnosis, known risk factors, and biomarkers in expected directions across both cohorts.
- Increasing SHIMMER scores correlated with increased prevalence of risk factors, complications, and medications (e.g., hypertension with atrial fibrillation SHIMMER).
- Biomarker levels for type 2 diabetes and gradations of earlier disease onset and decreased survival (coronary artery disease, schizophrenia) were revealed by rising SHIMMER.
Conclusions:
- A holistic, non-invasive marker (SHIMMER) derived from AI and routine clinical data captures multiple common diseases on a spectrum.
- SHIMMER quantifies disease risk, severity, onset, survival, sequelae, and treatment.
- This AI-driven approach offers a scalable and portable method for understanding disease spectra.
Keywords:
atrial fibrillationbiobankbreast cancerchronic diseaseclinical decision supportcoronary artery diseasedigital biomarkersdisease trajectory predictionelectronic health recordsmachine learningmigrainemulti-disease profilingpopulation healthportable health diagnosticsprecision medicinerheumatoid arthritisrisk stratificationschizophreniaspectral disease modelingtranslation to population healthtype 2 diabetesRelated Concept Videos
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