Q&A: A new model reveals hidden disease signatures and predicts health outcomes

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    "url": "https://medicalxpress.com/news/2026-07-qa-reveals-hidden-disease-signatures.html",
    "title": "Q&A: A new model reveals hidden disease signatures and predicts health outcomes - Medical Xpress",
    "excerpt": "by Mass General Brigham edited by Swati Mestri , reviewed by Andrew Zinin This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility: fact-checked peer-reviewed publication trusted source proofread Sarah Urbut, MD, Ph.D., of the Mass General Brigham Heart and Vascular Institute, is the lead author of a paper published in Nature , \"A Bayesian framework for longitudinal EHR and genetic discovery.\" Pradeep Natarajan, MD, MMSc, also of the Heart and Vascular Institute, is one of the co-senior authors, along with collaborators at Harvard Medical School, Harvard T.H. Chan School of Public Health, the Broad Institute of MIT and Harvard, and Dana-Farber Cancer Institute. In medicine, there is a tendency to view different specialties - or even different diseases within the same specialty - separately, rather than borrowing information across a patient's entire disease history. Different aspects of a patient's care are often managed in silos, and their medical history is frequently treated as a snapshot in time rather than as a process that continuously evolves. Moreover, patients are typically treated according to their diagnostic label (for example, \"coronary disease\" or \"diabetes\") rather than the biological processes driving their condition. This is a major issue...",
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    "summary": "Researchers from Mass General Brigham, along with collaborators, developed an advanced generative model called ALADYNOULLI to analyze patient histories and predict health outcomes.",
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