PDF(Pubmed)
Abstract：
BACKGROUND: Recent studies have demonstrated that individuals hospitalized due to COVID-19 can be affected by \"long-COVID\" symptoms for as long as one year after discharge.
OBJECTIVE: Our study objective is to identify data-driven clusters of patients using a novel, unsupervised machine learning technique.
METHODS: The study uses data from 437 patients hospitalized in New York City between March 3rd and May 15th of 2020. The data used was abstracted from medical records and collected from a follow-up survey for up to one-year post-hospitalization. Hospitalization data included demographics, comorbidities, and in-hospital complications. The survey collected long-COVID symptoms, and information on general health, social isolation, and loneliness. To perform the analysis, we created a graph by projecting the data onto eight principal components (PCs) and running the K-nearest neighbors algorithm. We then used Louvain\'s algorithm to partition this graph into non-overlapping clusters.
RESULTS: The cluster analysis produced four clusters with distinct health and social connectivity patterns. The first cluster (n = 141) consisted of patients with both long-COVID neurological symptoms (74%) and social isolation/loneliness. The second cluster (n = 137) consisted of healthy patients who were also more socially connected and not lonely. The third cluster (n = 96) contained patients with neurological symptoms who were socially connected but lonely, and the fourth cluster (n = 63) consisted entirely of patients who had traumatic COVID hospitalization, were intubated, suffered symptoms, but were socially connected and experienced recovery.
CONCLUSIONS: The cluster analysis identified social isolation and loneliness as important features associated with long-COVID symptoms and recovery after hospitalization. It also confirms that social isolation and loneliness, though connected, are not necessarily the same. Physicians need to be aware of how social characteristics relate to long-COVID and patient\'s ability to cope with the resulting symptoms.
OBJECTIVE: Our study objective is to identify data-driven clusters of patients using a novel, unsupervised machine learning technique.
METHODS: The study uses data from 437 patients hospitalized in New York City between March 3rd and May 15th of 2020. The data used was abstracted from medical records and collected from a follow-up survey for up to one-year post-hospitalization. Hospitalization data included demographics, comorbidities, and in-hospital complications. The survey collected long-COVID symptoms, and information on general health, social isolation, and loneliness. To perform the analysis, we created a graph by projecting the data onto eight principal components (PCs) and running the K-nearest neighbors algorithm. We then used Louvain\'s algorithm to partition this graph into non-overlapping clusters.
RESULTS: The cluster analysis produced four clusters with distinct health and social connectivity patterns. The first cluster (n = 141) consisted of patients with both long-COVID neurological symptoms (74%) and social isolation/loneliness. The second cluster (n = 137) consisted of healthy patients who were also more socially connected and not lonely. The third cluster (n = 96) contained patients with neurological symptoms who were socially connected but lonely, and the fourth cluster (n = 63) consisted entirely of patients who had traumatic COVID hospitalization, were intubated, suffered symptoms, but were socially connected and experienced recovery.
CONCLUSIONS: The cluster analysis identified social isolation and loneliness as important features associated with long-COVID symptoms and recovery after hospitalization. It also confirms that social isolation and loneliness, though connected, are not necessarily the same. Physicians need to be aware of how social characteristics relate to long-COVID and patient\'s ability to cope with the resulting symptoms.
摘要：
背景：最近的研究表明，因COVID-19住院的患者在出院后长达一年的时间内可能会受到“长期COVID”症状的影响。
目的：我们的研究目的是识别数据驱动的患者群，无监督机器学习技术。
方法：该研究使用了2020年3月3日至5月15日在纽约市住院的437名患者的数据。所使用的数据是从医疗记录中提取的，并从住院后长达一年的随访调查中收集。住院数据包括人口统计，合并症,和住院并发症。调查收集了长期的COVID症状，和一般健康信息，社会孤立,和孤独。要执行分析，我们通过将数据投影到八个主成分（PC）并运行K最近邻算法来创建图形。然后，我们使用Louvain的算法将该图划分为不重叠的簇。
结果：聚类分析产生了四个具有不同健康和社会连接模式的集群。第一组(n=141)由患有长期COVID神经症状(74%)和社交孤立/孤独的患者组成。第二组（n=137）由健康患者组成，他们也更有社交联系且不孤独。第三组（n=96）包含有神经系统症状的患者，他们与社会有联系但孤独，第四组（n=63）完全由患有创伤性COVID住院的患者组成，插管，有症状,但与社会有联系，经历了康复。
结论：聚类分析确定社会隔离和孤独感是与长期COVID症状和住院后恢复相关的重要特征。这也证实了社会孤立和孤独，虽然连接,不一定是一样的。医生需要意识到社会特征与长期COVID和患者应对由此产生的症状的能力之间的关系。
目的：我们的研究目的是识别数据驱动的患者群，无监督机器学习技术。
方法：该研究使用了2020年3月3日至5月15日在纽约市住院的437名患者的数据。所使用的数据是从医疗记录中提取的，并从住院后长达一年的随访调查中收集。住院数据包括人口统计，合并症,和住院并发症。调查收集了长期的COVID症状，和一般健康信息，社会孤立,和孤独。要执行分析，我们通过将数据投影到八个主成分（PC）并运行K最近邻算法来创建图形。然后，我们使用Louvain的算法将该图划分为不重叠的簇。
结果：聚类分析产生了四个具有不同健康和社会连接模式的集群。第一组(n=141)由患有长期COVID神经症状(74%)和社交孤立/孤独的患者组成。第二组（n=137）由健康患者组成，他们也更有社交联系且不孤独。第三组（n=96）包含有神经系统症状的患者，他们与社会有联系但孤独，第四组（n=63）完全由患有创伤性COVID住院的患者组成，插管，有症状,但与社会有联系，经历了康复。
结论：聚类分析确定社会隔离和孤独感是与长期COVID症状和住院后恢复相关的重要特征。这也证实了社会孤立和孤独，虽然连接,不一定是一样的。医生需要意识到社会特征与长期COVID和患者应对由此产生的症状的能力之间的关系。
