Nature重磅：魔改GPT-2，AI帮你预测未来20年健康风险，涉及1000+疾病

Core Insights - The article discusses the breakthrough research of the Delphi-2M model, which utilizes AI to predict individual health trajectories and disease risks over a 20-year period, based on personal medical history and lifestyle data [1][2][9]. Model Overview - Delphi-2M is based on a modified GPT-2 architecture, allowing for the prediction of over 1,256 diseases and mortality risks, achieving high accuracy in its predictions [5][6]. - The model's training relied on high-quality datasets, including 400,000 participants from the UK Biobank, ensuring its generalizability and reliability [4][6]. Validation and Performance - Internal validation showed an average AUC of 0.76 for most diseases, with mortality risk predictions reaching an AUC of 0.97, indicating near-perfect predictive capability [6]. - The model demonstrated strong cross-population applicability, maintaining relevant disease prediction patterns when applied to Danish data without parameter adjustments [6][9]. Long-term Prediction Capability - Unlike traditional models that predict risks for 1-5 years, Delphi-2M can simulate health trajectories for up to 20 years, showing high accuracy in predicting disease incidence rates for older populations [8][10]. - The model effectively distinguishes between high-risk and low-risk individuals based on historical health data, enhancing personalized risk assessment [8][10]. Privacy and Data Generation - Delphi-2M addresses privacy concerns by generating synthetic health data that mimics real population disease incidence patterns without revealing personal information [9][10]. - This capability allows for the training of other medical AI models while protecting individual privacy and optimizing data resource utilization [9][10]. Future Directions - The model's architecture is designed to integrate various data types, including genomic and imaging data, which could enhance its predictive capabilities as data integration progresses [11]. - Delphi-2M is positioned as a supportive tool for medical decision-making, emphasizing the need for integration with clinical expertise and patient preferences [11][12].