AI models analysed large-scale health data to personalise treatment


This study introduces ETHOS, a foundation AI model that treats electronic health records (EHRs) as a language to predict patient outcomes. By "tokenizing" patient histories—converting sequences of diagnoses, medications, and lab results into data streams—ETHOS learns to forecast future medical events using transformer architecture, the same technology behind Large Language Models.

Building on this core, the researchers developed the Adaptive Risk Estimation System (ARES) to generate real-time, personalized risk assessments. Tested on the massive MIMIC-IV dataset, the model significantly outperformed traditional clinical benchmarks in predicting critical outcomes like mortality, ICU admissions, and prolonged hospital stays. Unlike static scoring tools, ARES adapts dynamically as new data enters the record. Crucially, it includes an explainability module that highlights exactly which factors—such as a specific lab spike or medication change—drove the risk prediction. This demonstrates how generative AI can transform raw, unstructured medical data into actionable, personalized clinical intelligence.


Read the original article at https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giaf107/8268904

 

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