@article {Filipow2002881，作者= {Filipow，尼科尔和Davies，格温和主，埃莉诺和Sebire，尼尔J.和沃利斯，Colin和Ratjen费利克斯和Stanojevic，三社}，标题= {无监督聚类表型用于确定临床状态孩子患有囊性纤维化}，体积= {} 58，数= {2}，elocation-ID = {2002881}，年= {} 2021，D​​OI = {10.1183 / 13993003.02881-2020}，出版商= {欧洲呼吸学会}，188bet官网地址抽象= {背景囊性纤维化（CF）是其中基于肺功能的疾病严重程度的评估单独可能不适合多系统疾病。这项研究的目的是建立一个全面的机器学习算法来施加无监督聚类分析，以评估children.Methods全面的前瞻性收集的临床数据库（多伦多，加拿大）的临床状态独立肺功能。所定义的簇然后通过当前和未来的肺功能，未来住院风险，和未来的口服抗生素治疗肺源性恶化的风险相比较。的k最近邻（KNN）算法用于前瞻性分配集群。The methods were validated in a paediatric clinical CF dataset from Great Ormond Street Hospital (GOSH).Results The optimal cluster model identified four (A{\textendash}D) phenotypic clusters based on 12 200 encounters from 530 individuals. Two clusters (A and B) consistent with mild disease were identified with high forced expiratory volume in 1 s (FEV1), and low risk of both hospitalisation and pulmonary exacerbation treated with oral antibiotics. Two clusters (C and D) consistent with severe disease were also identified with low FEV1. Cluster D had the shortest time to both hospitalisation and pulmonary exacerbation treated with oral antibiotics. The outcomes were consistent in 3124 encounters from 171 children at GOSH. The KNN cluster allocation error rate was low, at 2.5\% (Toronto) and 3.5\% (GOSH).Conclusion Machine learning derived phenotypic clusters can predict disease severity independent of lung function and could be used in conjunction with functional measures to predict future disease trajectories in CF patients.Machine learning-derived clusters can be used to define clinical status in children with cystic fibrosis https://bit.ly/3nudlPG}, issn = {0903-1936}, URL = {//www.qdcxjkg.com/content/58/2/2002881}, eprint = {//www.qdcxjkg.com/content/58/2/2002881.full.pdf}, journal = {European Respiratory Journal} }