Heart failure (HF) is among the most costly diseases to our society, and the prevalence keeps on increasing these days. Early detection of HF plays a vital role in saving lives through adjusting lifestyles and drug interventions that can slow down disease progression or prevent HF. There are many cardiovascular risk factors associated with HF, and they often coexist. In this paper, we assess the predictive value of pathological factors for early HF detection through a social network based approach. We use electronic health records (collected from the project HeartCarer) and compute the similarity of risk factors. The similarity values are used to construct an unweighted and a weighted medical social network. The constructed medical social network is further divided into a HF high-risk group and HF low-risk group using a group division algorithm. Patients in the high-risk group will be suggested for early screening. To evaluate the prediction value of our method, we perform four experiments based on real world data. The results demonstrate the high effectiveness of our method on heart failure risk assessment, with the best accuracy close to 90%.

心力衰竭（HF）是对我们的社会而言代价最高的疾病之一，如今，患病率一直在上升。早期发现HF在通过调整生活方式和药物干预措施（可减慢疾病进程或预防HF）挽救生命方面起着至关重要的作用。HF有许多心血管危险因素，它们通常并存。在本文中，我们通过基于社交网络的方法评估了病理因素对早期HF检测的预测价值。我们使用电子健康记录（从HeartCarer项目收集）并计算风险因素的相似性。相似度值用于构建未加权和加权医疗社交网络。使用组划分算法将所构建的医学社交网络进一步划分为HF高风险组和HF低风险组。建议高危组的患者进行早期筛查。为了评估我们方法的预测值，我们基于现实世界的数据进行了四个实验。结果证明了我们的方法在心力衰竭风险评估中的高效性，最佳准确率接近90％。