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Background: Hematopoietic stem cell transplantation (HSCT) is a procedure with high morbidity and mortality. Identifying patients for maximum benefit and risk assessment is crucial in the decision‐making process. This has led to the development of predictive risk models for HSCT in adults, which have limitations when applied to pediatric population. Our goal was to develop an automatic learning algorithm to predict survival in children with malignant disorders undergoing HSCT. Methods: We studied allogenic HSCTs performed on children with malignant disorders at a third‐level hospital between 1991 and 2021. Survival was analyzed using the Kaplan–Meier method, log‐rank test for the univariate analysis, and Cox regression for the multivariate analysis. A prognostic index was constructed based on these findings. Lastly, we constructed a predictive model using a random forest algorithm to forecast 1‐year survival after HSCT. Results: We analyzed 229 HSCTs in 201 patients with a median follow‐up of 1.64 years. Variables that impacted on the multivariate analysis were older age (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.12–1.76, p =.003), oldest period of HSCT (HR 0.46, 95% CI 0.29–0.73, p <.001), and mismatched donor (HR 2.65, 95% CI 1.51–4.65, p =.001). Our prognostic index was associated with 3‐year overall survival (OS; p <.001). A random forest was developed using as variables: diagnosis, age, year of HSCT, time from diagnosis to HSCT, disease stage, donor type, and conditioning. This achieved 72% accuracy in predicting 1‐year OS. Conclusions: Our index and random forest was effective in predicting 1‐year survival. However, further validation in diverse populations is necessary to establish their generalizability. [ABSTRACT FROM AUTHOR]

In 5G and beyond radios, the increased bandwidth, the fast-changing waveform scenarios, and the operation of large array multiple-input multiple-output (MIMO) transmitter architectures have challenged both the polynomial and the artificial neural network (ANN) MIMO adaptive digital predistortion (DPD) schemes. This article proposes training data selection methods and dimensionality reduction techniques that can be combined to enable relevant reductions of the DPD training time and the implementation complexity for MIMO transmitter architectures. In this work, the combination of an efficient uncorrelated equation selection (UES) mechanism together with orthogonal least squares (OLS) is proposed to reduce the training data length and the number of basis functions at every behavioral modeling matrix in the polynomial MIMO DPD scheme. For ANN MIMO DPD architectures, applying UES and principal component analysis (PCA) is proposed to reduce the input dataset length and features, respectively. The UES-OLS and the UES-PCA techniques are experimentally validated for a $2 \,\times \, 2$ MIMO test setup with strong power amplifier (PA) input and output crosstalk. [ABSTRACT FROM AUTHOR]

This paper presents an estimation/adaptation method based on the adaptive principal component analysis (APCA) technique to guarantee the identification of the minimum necessary parameters of a digital predistorter. The proposed estimation/adaptation technique is suitable for online field-programmable gate array or system on chip implementation. By exploiting the orthogonality of the resulting transformed matrix obtained with the APCA technique, it is possible to reduce the number of coefficients to be estimated which, at the same time, has a beneficial regularization effect by preventing ill-conditioning or overfitting problems. Therefore, this identification/adaptation method enhances the robustness of the parameter estimation and simplifies the adaptation by reducing the number of estimated coefficients. Due to the orthogonality of the new basis, these parameters can be estimated independently, thus allowing for scalability. Experimental results will show that it is possible to determine the minimum number of parameters to be estimated in order to meet the targeted linearity levels while ensuring a robust well-conditioned identification. Moreover, the results will show how thanks to the orthogonality property of the new basis functions, the coefficients of the digital predistorter can be estimated independently. This allows to tradeoff the digital predistorter adaptation time versus performance and hardware complexity. [ABSTRACT FROM AUTHOR]