Browsing by Subject "Feature importance"

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    Survival analysis in hematopoietic stem cell transplantation for multiple myeloma: methodology and survival predictions
    (Springer Nature, 2025-04-25) Belmonte, José María; Blanquer Blanquer, Miguel; Bernabé García, Gregorio; Jiménez Barrionuevo, Fernando; García Carrasco, José Manuel; Ingeniería y Tecnología de Computadores
    This work explores the application of Survival Analysis in the context of hematopoietic stem cell transplantation for multiple myeloma to enhance the predictive capacity and interpretability of transplant outcomes and inspect the patients’ overall survival. Our methodology uses all the proposed Survival Analysis models. These models are used to conduct a feature importance analysis and do some survival predictions with the interpretation of patient outcomes. The dataset, comprising 254 instances and 15 attributes, includes medical information collected from multiple myeloma patients before hematopoietic stem cell transplantation procedures. The primary objective of this work is to assess the robustness of Survival Analysis models with our data based on the concordance index metric. Through feature importance analysis, it has been revealed that variables such as the International Staging System, treatment lines, age, and disease relapse play pivotal roles in determining patient survival post-transplant. Survival predictions have been conducted for three distinct cases from the dataset, evaluating the risks patients may encounter following their treatments. These results have been validated by healthcare professionals, underscoring the reliability and applicability of this study’s findings in medical scenarios.
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    Survival risk prediction in hematopoietic stem cell transplantation for multiple myeloma
    (De Gruyter, 2025-06-03) Belmonte, José María; Blanquer Blanquer, Miguel; Bernabé García, Gregorio; Jiménez Barrionuevo, Fernando; García Carrasco, José Manuel; Ingeniería y Tecnología de Computadores
    This paper investigates the application of Survival Analysis (SA) techniques to forecast outcomes after autologous Hematopoietic Stem Cell Transplantation (aHSCT) for Multiple Myeloma (MM). By leveraging six SA models, we examine their predictive capabilities, measured through the Concordance Index (C-index) metric. Beyond evaluating model performance, we analyze feature importance using permutation and SHAP methods, highlighting key clinical factors such as treatment history, disease stage, and prior disease progression or relapse as critical predictors of survival. The findings suggest that while all models performed well based on the C-index, a detailed examination revealed variations in how each model processed data. Specifically, the Coxnet and Random Survival Forest models exhibited a more thorough use of clinical variables, whereas the gradient boosting models appeared to rely on a narrower range of features, potentially limiting their ability to differentiate between patients with comparable profiles. Risk predictions categorized patients into low, moderate, and high-risk levels. For lower-risk patients, the procedure showed positive outcomes, while higher-risk individuals were predicted to have limited survival benefits, recommending alternative treatments. Lastly, we propose future research to expand these models into time-to-event estimations, offering additional support for decision-making by predicting patient life expectancy post-transplant, considering their pre-transplant clinical attributes.