Analysis of Prognostic Factors for Mortality in Patients With Gastrointestinal Bleeding: Application of Machine Learning Tools
https://doi.org/10.35401/2541-9897-2024-9-4-68-76
Abstract
Introduction: Treatment of upper gastrointestinal bleeding (UGIB) is a complex challenge due to the wide range of causes and factors affecting hospitalization outcomes.
Objective: To study the impact of various factors on 30-day hospital outcomes using machine learning (ML) tools.
Materials and methods: We compiled a retrospective data set that includes clinical, laboratory, and imaging data of 101 patients. The database was divided into 2 groups by UGIB etiology: ulcer and variceal bleedings. Both etiological groups were processed using ML tools in 2 steps: imputation by the MICE (multiple imputation by chained equations) model and factor importance analysis using the Random Forest model.
Results: Analysis revealed that the most prognostically valuable parameters in both groups were well-known mortality predictors and emerging predictive factors, such as creatinine, blood pressure, activated partial thromboplastin time, level of consciousness, urea, lactate, comorbidity status, procalcitonin, ferritin, and total protein.
Conclusions: The application of advanced tools confirmed the significance of popular and validated mortality predictors and contributed to the development of predictors, both explored and unexplored ones.
About the Authors
A. O. Ismati
Samarkand State Medical University
Uzbekistan
Uzbekistan
Amir O. Ismati - Basic Doctoral Student, Assistant Professor at the Department of Surgical Diseases, Samarkand State Medical University.
Samarkand
V. D. Anosov
O.M. Filatov City Clinical Hospital No. 15
Russian Federation
Russian Federation
Viktor D. Anosov - Cand. Sci. (Med.), Deputy Chief Physician for Surgical Care, O.M. Filatov City Clinical Hospital No. 15.
Moscow
S. E. Mamarajabov
Samarkand State Medical University
Uzbekistan
Uzbekistan
Sobirjon E. Mamarajabov - Dr. Sci. (Med.), Dean of the Faculty of International Education, Head of the Department of Surgical Diseases, Samarkand State Medical University.
Amir Temur ko’chasi, 18, Samarkand, 140100
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Review
For citations:
Ismati A.O., Anosov V.D., Mamarajabov S.E. Analysis of Prognostic Factors for Mortality in Patients With Gastrointestinal Bleeding: Application of Machine Learning Tools. Innovative Medicine of Kuban. 2024;(4):68-76. (In Russ.) https://doi.org/10.35401/2541-9897-2024-9-4-68-76
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