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Vol. 46. Núm. S4.
HEMO 2024
Páginas S259-S260 (outubro 2024)
Vol. 46. Núm. S4.
HEMO 2024
Páginas S259-S260 (outubro 2024)
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REVEALING HIDDEN PATTERNS: HOW UNSUPERVISED MACHINE LEARNING AND MCA PREDICT SURVIVAL IN NODAL PERIPHERAL T-CELL LYMPHOMA PATIENTS
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CO Reicherta,b,c, G Carneiroa, HF Cullera,b, FA Freitasa,b, VG Rochaa,b, CA Murga-Zamalload, LAPC Lagea,b, R Olimpioc, J Pereiraa,d
a Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, Brazil
b Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo, Brazil
c MBA Data Science e Analytics, Universidade de São Paulo (USP), Piracicaba, Brazil
d Universidade de Illinois, Chicago, United States
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Vol. 46. Núm S4

HEMO 2024

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Unsupervised machine learning techniques are employed to understand patterns and behaviors of variables in databases. Multiple Correspondence Analysis (MCA), an extension of correspondence analysis, can be used to verify the association of categorical variables and their categories. In this study, we evaluated the relationship between clinical-demographic variables of 154 patients with nodal peripheral T-cell lymphoma (PTCL) to understand how these variables relate to the unfavorable clinical outcome, death.

Methodology

MCA was used to reduce the dimensionality of the real world database by creating two dimensions, 1 and 2, which were subsequently categorized into group 1 and group 2. Dimension 1, comprising ECOG, IPI, treatment, overall response, remission, and bone marrow transplant, represented about 30% of the total observed variance. Survival analysis was conducted to assess the association between these groups and overall survival (OS) and mortality rate.

Results

The categories of dimension 1, group 1 and group 2, were associated with OS and mortality rate, with group 1 being associated with 5.5 months (95% CI: 2.70 – 8.40) of OS, while group 2 had a survival time of 277.20 months (95% CI: 91.21 – 463.12). Moreover, the mortality rate in group 1 was 87% (n = 67) and in group 2 it was 36% (n = 28) (p < 0.001). The risk of death for group 1 was 11.11 times (95% CI: 9.75-18.30; β= 2.41; p < 0.001).

Discussion

These findings indicate a significant disparity in survival outcomes based on the clinical-demographic profiles of the patients. Group 1, characterized by poorer clinical indicators, exhibited substantially shorter OS and higher mortality rates. The ability of MCA to effectively reduce dimensionality while preserving the clinical relevance of the variables underscores its utility in identifying key prognostic factors.

Conclusion

The MCA technique was effective in reducing the dimensionality of the database, maintaining the clinical characteristics of the variables robustly for use in Cox regression. This approach can aid in the identification of high-risk patient groups and inform treatment strategies aimed at improving clinical outcomes.

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Idiomas
Hematology, Transfusion and Cell Therapy
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