Methods and systems for predicting onset and progression of medical conditions

A predictive model using normalized blood-test values addresses the challenge of rising healthcare costs by enabling early detection and intervention for medical conditions, improving patient outcomes and reducing costs through personalized treatment recommendations.

WO2026080914A1 Publication Date: 2026-04-16MOR RES APPL LTD +1
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Patent Information

Application Number
PCT/US2025/050618
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-10-10
Filing Date
2025-10-10
Publication Date
2026-04-16

AI Technical Summary

Technical Problem

Healthcare systems face rising costs due to aging populations and increasing chronic diseases, necessitating improved methods for predicting the onset and progression of medical conditions to enable early detection and intervention, thereby reducing costs and improving patient outcomes.

Method used

A computer-implemented method using time-stamped blood-test values, normalized to laboratory-specific intervals, to train predictive models that generate risk scores for medical condition onset or progression, allowing for personalized treatment recommendations based on age- and sex-adjusted standardized values.

Benefits of technology

Enables early detection and intervention, reducing the need for costly late-stage treatments and hospitalizations, improving patient outcomes, and lowering overall healthcare costs by identifying individuals at high risk for various medical conditions.

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Abstract

Provided herein are computer-implemented methods and systems for predicting the onset or progression of medical conditions using data from electronic health records (EHR) or electronic medical records (EMR), such as time-stamped blood-test values, in combination with trained predictive models. In some aspects, the predictive models comprise statistical or machine-learning algorithms trained using supervised learning on labeled clinical data. The disclosed methods and systems can generate individualized risk scores, classify subjects relative to operating thresholds, and optionally generate or record treatment recommendations in an EHR or EMR. The methods and systems can be applied to human or non-human subjects, enabling early detection of medical conditions, personalized risk assessment, and integration with healthcare information infrastructure.
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