Two-dimensional Quantitative Profiling of Cell Morphology in Serous Effusion Specimens Using Unsupervised Machine Learning Analysis

Objective: To explore the efficacy of anticoagulation in improving outcomes and safety of Coronavirus disease 2019 (COVID-19) patients in subgroups identified by clinical-based stratification and unsupervised machine learning.Methods: This single-center retrospective cohort study unselectively reviewed 2,272 patients with COVID-19 admitted to the Tongji Hospital between Jan 25 and Mar 23, 2020. The association between AC treatment and outcomes was investigated in the propensity score (PS) matched cohort and the full cohort by inverse probability of treatment weighting (IPTW) analysis. Subgroup analysis, identified by clinical-based stratification or unsupervised machine learning, was used to identify sub-phenotypes with meaningful clinical features and the target patients benefiting most from AC.Results: AC treatment was associated with lower in-hospital death risk either in the PS matched cohort or by IPTW analysis in the full cohort. A higher incidence of clinically relevant non-major bleeding (CRNMB) was observed in the AC group, but not major bleeding. Clinical subgroup analysis showed that, at admission, severe cases of COVID-19 clinical classification, mild acute respiratory distress syndrome (ARDS) cases, and patients with a D-dimer level ≥0.5 μg/mL, may benefit from AC. During the hospital stay, critical cases and severe ARDS cases may benefit from AC. Unsupervised machine learning analysis established a four-class clustering model. Clusters 1 and 2 were non-critical cases and might not benefit from AC, while clusters 3 and 4 were critical patients. Patients in cluster 3 might benefit from AC with no increase in bleeding events. While patients in cluster 4, who were characterized by multiple organ dysfunction (neurologic, circulation, coagulation, kidney and liver dysfunction) and elevated inflammation biomarkers, did not benefit from AC.Conclusions: AC treatment was associated with lower in-hospital death risk, especially in critically ill COVID-19 patients. Unsupervised learning analysis revealed that the most critically ill patients with multiple organ dysfunction and excessive inflammation might not benefit from AC. More attention should be paid to bleeding events (especially CRNMB) when using AC.

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Unsupervised Machine Learning Analysis of Urinary Transcriptome Reveals Distinct Genotypic Clustering in Surgical Sepsis

Journal of the American College of Surgeons ◽

10.1016/j.jamcollsurg.2017.07.137 ◽

2017 ◽

Vol 225 (4) ◽

pp. S66-S67

Author(s):

Nicholas Lysak ◽

Ashkan Ebadi ◽

Sabyasachi Bandyopadhyay ◽

Tezcan Ozrazgat-Baslanti ◽

Larysa Sautina ◽

...

Keyword(s):

Machine Learning ◽

Unsupervised Machine Learning ◽

Learning Analysis

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Recreation of the periodic table with an unsupervised machine learning algorithm

Scientific Reports ◽

10.1038/s41598-021-81850-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Minoru Kusaba ◽

Chang Liu ◽

Yukinori Koyama ◽

Kiyoyuki Terakura ◽

Ryo Yoshida

Keyword(s):

Machine Learning ◽

Periodic Table ◽

Data Science ◽

Learning Algorithm ◽

Chemical Properties ◽

Human Cognition ◽

Machine Learning Algorithm ◽

Two Dimensional ◽

Generative Topographic Mapping ◽

Unsupervised Machine Learning

AbstractIn 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev. In terms of data science, his achievement can be viewed as a successful example of feature embedding based on human cognition: chemical properties of all known elements at that time were compressed onto the two-dimensional grid system for a tabular display. In this study, we seek to answer the question of whether machine learning can reproduce or recreate the periodic table by using observed physicochemical properties of the elements. To achieve this goal, we developed a periodic table generator (PTG). The PTG is an unsupervised machine learning algorithm based on the generative topographic mapping, which can automate the translation of high-dimensional data into a tabular form with varying layouts on-demand. The PTG autonomously produced various arrangements of chemical symbols, which organized a two-dimensional array such as Mendeleev’s periodic table or three-dimensional spiral table according to the underlying periodicity in the given data. We further showed what the PTG learned from the element data and how the element features, such as melting point and electronegativity, are compressed to the lower-dimensional latent spaces.

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