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Unsupervised Machine Learning Elicits Patient Archetypes in a Primary Percutaneous Coronary Intervention Service
Aleeha Iftikhar
, Raymond Bond
, Victoria McGilligan
, Khaled Rjoob
, Stephen J. Leslie
, Charles Knoery
, Anne McShane
, Aaron Peace
Division of Biomedical Sciences
Centre for Rural Health Sciences
科研成果
:
Paper
›
同行评审
综述
指纹
指纹
探究 'Unsupervised Machine Learning Elicits Patient Archetypes in a Primary Percutaneous Coronary Intervention Service' 的科研主题。它们共同构成独一无二的指纹。
分类
加权
按字母排序
Biochemistry, Genetics and Molecular Biology
Cluster Analysis
100%
Mortality Rate
100%
Unsupervised Machine Learning
100%
Blood Flow
50%
Electrocardiogram
50%
Clinical Decision Making
50%
Nursing and Health Professions
Unsupervised Machine Learning
100%
Percutaneous Coronary Intervention
100%
Patient Referral
75%
Mortality Rate
25%
Cluster Analysis
25%
Elderly Patient
12%
Electrocardiogram
12%
Clinical Decision Making
12%
Female Patient
12%
Blood Flow
12%
Medicine and Dentistry
Primary Percutaneous Coronary Intervention
100%
Patient Referral
75%
Door-to-Balloon
50%
Mortality Rate
25%
Cluster Analysis
25%
Blood Flow
12%
Clinician
12%
Coronary Artery
12%
Electrocardiogram
12%
Clinical Decision Making
12%
Elderly Patient
12%
Pharmacology, Toxicology and Pharmaceutical Science
Mortality Rate
100%