重疾预警模型

本产品为经过数据训练的重大疾病预警模型，不提供数据，需要对接合法的数据源方，利用其公共卫生档案和诊疗、体检数据或医保数据，对个体进行精准重疾风险预警，实现多慢病一体化联动在线实时智能预测预警和个性化干预决策能力。国家健康医疗大数据研究院联合济南兴腾信息科技有限公司研发了《“一脑多端”智慧主动健康图脑引擎一体机》，聚焦心脑血管疾病和癌症等重大慢病防治工作，牵头承接国家重点研发主动健康与老龄化专项，融合大数据、人工智能、云计算等技术，经业内专家论证，一体机整体达到国际先进水平。一体机在流程设计上严格遵循了团体标准《疾病预测模型技术规范》，确保了其预测的准确性和可靠性，其核心部件图脑引擎则是基于国研院薛付忠团队提出的“慢病一体化因果知识图谱”和“边际因果驱动的逆概率加权贝叶斯网络cox模型”理论。运用这些前沿的科研成果，使一体机具备了深厚的理论基础。值得一提的是图脑引擎的训练数据来源于山东省时间跨度达十年的500万自然人群队列，这一庞大的数据集保证了模型的广泛适用性和实用性，在高噪稀疏的健康医疗大数据环境中，一体机图脑引擎能够表现出色，实现多慢病一体化联动在线实时智能预测预警和个性化干预决策，为全民健康的发展提供了有力支持。一体机可结合药物基因组数据对个体提供精准用药建议方案，优化患者的用药效果，降低药物副作用的发生。本成果可应用于公共卫生领域（重大慢病预警、用药推荐等）、健康保险领域（保险推荐、保险风控等）、企业健康管理领域（职工健康保障、用工风险分析等），市场前景广阔。This product is a data-trained major disease early warning model. It does not provide raw data, and requires integration with legitimate data providers to leverage their public health records, diagnostic, treatment and physical examination data, or medical insurance data for precise individual risk early warning of major diseases, enabling the capabilities of integrated real-time online intelligent prediction, early warning and personalized intervention decision-making for multiple chronic diseases. Developed jointly by the National Institute of Healthcare Big Data and Jinan Xingteng Information Technology Co., Ltd., the "One Brain, Multiple Terminals" Smart Active Health Brain-Engine All-in-One Machine focuses on the prevention and treatment of major chronic diseases such as cardiovascular and cerebrovascular diseases and cancer. It takes the lead in undertaking the National Key R&D Program for Active Health and Aging, integrating technologies including big data, artificial intelligence (AI) and cloud computing. Verified by industry experts, the all-in-one machine has reached the international advanced level overall. The all-in-one machine strictly complies with the group standard "Technical Specifications for Disease Prediction Models" in its process design, ensuring the accuracy and reliability of its predictions. Its core component, the Brain-Engine, is based on the theories of "Integrated Causal Knowledge Graph for Chronic Diseases" and "Marginal Causality-Driven Inverse Probability Weighted Bayesian Network Cox Model" proposed by the team led by Xue Fuzhong from the National Institute. Leveraging these cutting-edge research achievements, the all-in-one machine has a solid theoretical foundation. Notably, the training data for the Brain-Engine originates from a 5-million natural population cohort in Shandong Province with a 10-year time span. This large-scale dataset guarantees the wide applicability and practicality of the model. In the high-noise and sparse healthcare big data environment, the all-in-one machine's Brain-Engine can perform exceptionally well, realizing integrated real-time online intelligent prediction, early warning and personalized intervention decision-making for multiple chronic diseases, providing strong support for the development of universal health. The all-in-one machine can also incorporate pharmacogenomic data to provide individuals with precise medication recommendation plans, optimizing patients' therapeutic effects and reducing the incidence of adverse drug reactions. This achievement can be applied in public health sectors (major chronic disease early warning, medication recommendation, etc.), health insurance sectors (insurance recommendation, insurance risk control, etc.), and corporate health management sectors (employee health security, employment risk analysis, etc.), with broad market prospects.

搜集汇总

背景与挑战

背景概述

该产品是基于山东省500万自然人群队列数据训练的重大疾病预警模型服务，融合人工智能技术实现慢病风险预测和用药建议，适用于公共卫生、健康保险等领域。模型遵循行业标准，具有十年跨度的数据支持。