@文章{信息:doi/10.2196/13260，作者=“王小芳和张，严和郝，世英和郑，乐和廖，佳宇和叶，承印和夏，民杰和王，奥利弗和刘，Modi和Weng，何清和Duong, Son Q和Jin, Bo和Alfreds, Shaun T和Stearns, Frank和Kanov, Laura和Sylvester, Karl G和Widen, Eric和McElhinney, Doff B和Ling，雪峰B”，标题=“预测肺癌发生的1年风险:前瞻性研究使用来自缅因州的电子健康记录，期刊=“J Med Internet Res”，年=“2019”，月=“5”，日=“16”，卷=“21”，数=“5”，页=“e13260”，关键词=“肺癌;风险预测模型;电子健康记录;背景:肺癌是世界范围内癌症死亡的主要原因。早期发现有肺癌风险的个体对于降低死亡率至关重要。目的:本研究的目的是建立并验证一种前瞻性风险预测模型，以识别普通人群中未来1年内有新发肺癌风险的患者。方法:从缅因州健康信息交换网络中提取个人患者电子健康记录(EHRs)的数据。研究人群包括2016年4月1日至2018年3月31日期间至少有一次电子病历的患者，且无肺癌病史。建立回顾性队列(N=873,598)和前瞻性队列(N=836,659)进行模型构建和验证。 An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual to quantify the probability of a new incident lung cancer diagnosis from October 1, 2016, to September 31, 2017. The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018. Results: The model had an area under the curve (AUC) of 0.881 (95{\%} CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07{\%}) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14{\%}). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer. Conclusions: We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance. ", issn="1438-8871", doi="10.2196/13260", url="//www.mybigtv.com/2019/5/e13260/", url="https://doi.org/10.2196/13260", url="http://www.ncbi.nlm.nih.gov/pubmed/31099339" }