@文章{信息:doi/10.2196/24473，作者=“Andy, Anietie U和Guntuku, Sharath C和Adusumalli, Srinath和Asch, David A和Groeneveld, Peter W和Ungar, Lyle H和Merchant, Raina M”，标题=“使用社交媒体数据预测心血管风险:机器学习模型的性能评估”，期刊=“JMIR Cardio”，年=“2021”，月=“Feb”，日=“19”，卷=“5”，数=“1”，页=“e24473”，关键词=“ASCVD;机器学习;自然语言处理;动脉粥样硬化;心血管疾病;社交媒体语言;背景:目前的动脉粥样硬化性心血管疾病(ASCVD)预测模型存在局限性;因此，人们正在努力提高ASCVD模型的歧视性。目的:我们试图评估与合并队列风险方程(pce)相比，社交媒体帖子在预测ASCVD 10年风险方面的歧视性力量。方法:我们同意在城市学术急诊科接受治疗的患者分享他们的Facebook帖子和电子医疗记录(emr)。 We retrieved Facebook status updates up to 5 years prior to study enrollment for all consenting patients. We identified patients (N=181) without a prior history of coronary heart disease, an ASCVD score in their EMR, and more than 200 words in their Facebook posts. Using Facebook posts from these patients, we applied a machine-learning model to predict 10-year ASCVD risk scores. Using a machine-learning model and a psycholinguistic dictionary, Linguistic Inquiry and Word Count, we evaluated if language from posts alone could predict differences in risk scores and the association of certain words with risk categories, respectively. Results: The machine-learning model predicted the 10-year ASCVD risk scores for the categories <5{\%}, 5{\%}-7.4{\%}, 7.5{\%}-9.9{\%}, and ≥10{\%} with area under the curve (AUC) values of 0.78, 0.57, 0.72, and 0.61, respectively. The machine-learning model distinguished between low risk (<10{\%}) and high risk (>10{\%}) with an AUC of 0.69. Additionally, the machine-learning model predicted the ASCVD risk score with Pearson r=0.26. Using Linguistic Inquiry and Word Count, patients with higher ASCVD scores were more likely to use words associated with sadness (r=0.32). Conclusions: Language used on social media can provide insights about an individual's ASCVD risk and inform approaches to risk modification. ", issn="2561-1011", doi="10.2196/24473", url="http://cardio.www.mybigtv.com/2021/1/e24473/", url="https://doi.org/10.2196/24473", url="http://www.ncbi.nlm.nih.gov/pubmed/33605888" }