摘要
一位新闻记者兼机器人与机器学习每日新闻编辑每日新闻-心脏病和糖尿病的新研究-心力衰竭是一篇报道的主题。根据来自中国成都的新闻，NewsRx记者，Research State，“缺乏准确识别老年心律失常患者心力衰竭再入院风险的工具。本研究的目的是建立和比较LACE[Len Gth of stay(‘L’)，急性(紧急)入院(‘A’)的表现。Charlson合并症指数（‘C’）和过去6个月内急诊科就诊次数（‘E’）]指数和机器学习预测老年心律失常患者心力衰竭再入院1年。我们的新闻记者引用成都市第二人民医院的研究，“入选2018-06/1-2020年5月31日四川省人民医院住院的老年心律失常患者，计算每位患者的LACE指数，计算接收R操作特征曲线下面积(AUROC)，采用6种机器学习算法。结合三种变量选择方法和出院时的临床相关特征，建立了Machine学习模型，用AUROC和Precision-Recall曲线下面积(AUPRC)评价辨别力，用Shapley相加解释(SHAP)分析解释这些特征的贡献，共纳入523例患者。LACE指数的AUROC为0.5886.,AUROC为0.7571,AUPRC为0.4096.。1年再入院最重要的预测因素是文化程度、总三碘甲状腺原氨酸(TT3)、天冬氨酸转氨酶/丙氨酸转氨酶(AST/ALT)、用药次数(NOM)和甘油三酯(TG)水平。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Heart Disorders and Di seases - Heart Failure is the subject of a report. According to news originating from Chengdu, People’s Republic of China, by NewsRx correspondents, research st ated, “There is a lack of tools for accurately identifying the risk of readmissi on for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [len gth of stay (‘L’), acute (emergent) admission (‘A’), Charlson comorbidity index (‘C’) and visits to the emergency department during the previous 6 months (‘E’)] index and machine learning in predicting 1 year readmission for heart failure in elderly patients with arrhythmia.” Our news journalists obtained a quote from the research from Chengdu Second Peop le’s Hospital, “Elderly patients with arrhythmia who were hospitalized at Sichua n Provincial People’s Hospital between 1 June 2018 and 31 May 2020 were enrolled . The LACE index was calculated for each patient, and the area under the receive r operating characteristic curve (AUROC) was calculated. Six machine learning al gorithms, combined with three variable selection methods and clinically relevant features available at the time of hospital discharge, were used to develop mach ine learning models. AUROC and area under the precision-recall curve (AUPRC) wer e used to assess discrimination. Shapley additive explanations (SHAP) analysis w as used to explain the contributions of the features. A total of 523 patients we re enrolled, and 108 patients experienced 1 year hospital readmission for heart failure. The AUROC of the LACE index was 0.5886. The complete machine learning m odel had the best predictive performance, with an AUROC of 0.7571 and an AUPRC o f 0.4096. The most important predictors for 1 year readmission were educational level, total triiodothyronine (TT3), aspartate aminotransferase/alanine aminotra nsferase (AST/ALT), number of medications (NOM) and triglyceride (TG) level.”
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