On similarity measures for cluster analysis in clinical laboratory examination databases

2003 ◽  
Author(s):  
S. Hirano ◽  
Xiaoguang Sun ◽  
S. Tsumoto
Keyword(s):  
Cluster Analysis ◽  
Clinical Laboratory ◽  
Similarity Measures ◽  
Laboratory Examination

scholarly journals Correlation between lung infection severity and clinical laboratory indicators in patients with COVID-19: a cross-sectional study based on machine learning

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xingrui Wang ◽  
Qinglin Che ◽  
Xiaoxiao Ji ◽  
Xinyi Meng ◽  
Lang Zhang ◽  
...  
Keyword(s):  
Peripheral Blood Lymphocyte ◽  
Peripheral Blood ◽  
Blood Lymphocyte ◽  
Clinical Laboratory ◽  
Lung Infection ◽  
Cross Sectional Study ◽  
Ct Images ◽  
Sectional Study ◽  
Laboratory Examination ◽  
Cross Sectional

Abstract Background Coronavirus disease 2019 (COVID-19) has caused a global pandemic that has raised worldwide concern. This study aims to investigate the correlation between the extent of lung infection and relevant clinical laboratory testing indicators in COVID-19 and to analyse its underlying mechanism. Methods Chest high-resolution computer tomography (CT) images and laboratory examination data of 31 patients with COVID-19 were extracted, and the lesion areas in CT images were quantitatively segmented and calculated using a deep learning (DL) system. A cross-sectional study method was carried out to explore the differences among the proportions of lung lobe infection and to correlate the percentage of infection (POI) of the whole lung in all patients with clinical laboratory examination values. Results No significant difference in the proportion of infection was noted among various lung lobes (P > 0.05). The POI of total lung was negatively correlated with the peripheral blood lymphocyte percentage (L%) (r = − 0.633, P < 0.001) and lymphocyte (LY) count (r = − 0.555, P = 0.001) but positively correlated with the neutrophil percentage (N%) (r = 0.565, P = 0.001). Otherwise, the POI was not significantly correlated with the peripheral blood white blood cell (WBC) count, monocyte percentage (M%) or haemoglobin (HGB) content. In some patients, as the infection progressed, the L% and LY count decreased progressively accompanied by a continuous increase in the N%. Conclusions Lung lesions in COVID-19 patients are significantly correlated with the peripheral blood lymphocyte and neutrophil levels, both of which could serve as prognostic indicators that provide warning implications, and contribute to clinical interventions in patients.

scholarly journals Using Clustering as a Tool: Mixed Methods in Qualitative Data Analysis

2015 ◽  
Author(s):  
Laura Macia
Keyword(s):  
Cluster Analysis ◽  
Mixed Methods ◽  
Data Analysis ◽  
Qualitative Data ◽  
Similarity Measures ◽  
Cluster Solution ◽  
Clustering Methods ◽  
Qualitative Data Analysis ◽  
Research Project ◽  
Detailed Explanation

In this article I discuss cluster analysis as an exploratory tool to support the identification of associations within qualitative data. While not appropriate for all qualitative projects, cluster analysis can be particularly helpful in identifying patterns where numerous cases are studied. I use as illustration a research project on Latino grievances to offer a detailed explanation of the main steps in cluster analysis, providing specific considerations for its use with qualitative data. I specifically describe the issues of data transformation, the choice of clustering methods and similarity measures, the identification of a cluster solution, and the interpretation of the data in a qualitative context.

Proving the Accuracy and Legal Liability of Clinical Laboratory Examination Results Using Automatic Tools

SOEPRA ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 358
Author(s):  
Yusi Luluk Rahmania ◽  
Tjahjono Kuntjoro ◽  
Valentinus Suroto
Keyword(s):  
Clinical Laboratory ◽  
Legal Liability ◽  
Laboratory Examination

Cluster Analysis of Wind Zones Using Various Similarity Measures

2021 ◽  
Vol 25 (3) ◽  
pp. 111-117
Author(s):  
Jin-Young Kim ◽  
◽  
Sujin Hwang ◽  
Chang Ki Kim ◽  
Hyun-Goo Kim
Keyword(s):  
Cluster Analysis ◽  
Similarity Measures

Clusters

2017 ◽  
pp. 179-190
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Statistical Data ◽  
Similarity Measures ◽  
Trial And Error ◽  
Multi Objective Optimization ◽  
Statistical Data Analysis ◽  
Multi Objective ◽  
Common Technique

Cluster analysis, which we approach in this chapter, is the task of grouping a set of objects in such a way that objects in the same group or cluster are more similar to each other than to those in other groups or clusters. It is a common technique for statistical data analysis. Cluster analysis can be achieved by various algorithms that might differ significantly. Therefore, cluster analysis as such is not a trivial task. It is an interactive multi-objective optimization that involves trial and error. Therefore, in cluster analysis, the clustering of subjects or variables are made from similarity measures or dissimilarity (distance) between two subjects initially, and later between two clusters. These groups can be done using hierarchical or non-hierarchical techniques.

Greeks grapple with responsibility

2015 ◽  
Vol 31 (10) ◽  
pp. 6-8
Keyword(s):  
Cluster Analysis ◽  
Corporate Responsibility ◽  
Health And Safety ◽  
Similarity Measures ◽  
Content Type ◽  
Production Methods ◽  
Pertinent Information ◽  
Socially Responsive ◽  
Corporate Social ◽  
Practical Implications

Purpose – This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach – This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings – Key findings denote occupational health and safety, benign environmental management as well as responsible production methods as the top corporate social responsibility (CSR) priorities. Two different CSR viewpoints emerged from the factor analysis reflecting a pragmatic and a more socially responsive interpretation of corporate responsibility. Cluster analysis confirmed such contrasting perspectives, allowing the partition of data in distinctive groups according to the relative inclination on either of the identified viewpoints. Similarity measures obtained from cluster analysis also verified the different CSR positions. Practical implications – The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations. Originality/value – The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Groupings of Psychiatric Nomenclature Classifications: Cluster Analyses of Psychiatric Subjective Ratio Scales

1969 ◽  
Vol 25 (2) ◽  
pp. 351-355
Author(s):  
Leroy A. Stone
Keyword(s):  
Cluster Analysis ◽  
Similarity Measures ◽  
External Stress ◽  
Cluster Analyses ◽  
Functional Psychosis ◽  
Ratio Scales ◽  
Impairment Severity ◽  
Degree Of Similarity

Three psychiatric judgmental ratio scales (impairment-severity, constitutionality, and susceptibilty to external stress) involving 15 functional psychosis classifications as stimuli were unidimensionally cluster analyzed. These scales were based on the scaled judgments (magnitude estimations) of 87 senior psychiatrists. Four-five clusters emerged with each scale. These analyses were computed from similarity measures (derived from scale values) which denoted degree of similarity between stimuli on a scale. A multidimensional cluster analysis based on averaged (across scales) similarity measures was also accomplished. All cluster analyses (unidimensional and multidimensional) appeared to group stimuli (classifications) into meaningful clusters.

scholarly journals APPLICATION OF NEURAL NETWORKS IN MEDICAL DIAGNOSTICS

2021 ◽  
pp. 40-51
Author(s):  
V. A. Sapozhkov ◽  
O. N. Budadin ◽  
A. S. Churilova ◽  
B. F. Falkov ◽  
Zh. Yu. Sapozhkova
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Laboratory ◽  
Medical Diagnostics ◽  
Automation System ◽  
Laboratory Examination ◽  
Artificial Neural ◽  
Personalized Approach ◽  
Diagnostic Outcomes

This article discusses the possibilities of application of artificial neural networks to solve problems of increasing the diagnostic outcomes in clinical laboratory examination. High diagnostic sensitivity (96 %) and diagnostic accuracy (89.5 %) of the results were shown on a large amount of cellular material digitized by artificial intelligence microscopy automation system like the Vision Cyto Pap. The high resolution and sharpness of digital slides, the mode of viewing objects (cells) in the gallery, quick access to the results of preclassification, all of these factors together allow to reduce turnearound time in more than 2.5 times reducing disadvantages of the microscopy.<br>Application of artificial neural networks does not substitute a doctor’s skills. The role in validation of reports eligible only for cytopathologist. This concept indicates a carefully approach for staff working with a microscope, respectful attitude to them professional skills, and highlights a personalized approach to patients.

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