Inputazio tekniken errendimenduaren ebaluazioa bi neurketako luzeranzko datuetan

Neurketa errepikatuetan oinarrituriko behaketa-ikerketak menpeko aldagaien aldaketak denboran zehar aztertzeko erabiltzen dira. Bi neurketa baizik bakarrik egiten ez direnean, ikerketa helburu nagusietariko bat izan daiteke menpeko aldagaiaren batez besteko aldaketa aurresaten dituzten faktoreak zehaztea. Menpeko aldagaian faltako balioak ohikoak dira ikerketa mota hauetan, behaturiko datuen analisiaren emaitzak alboratuak gerta daitezkeelarik. Lan honetan inputazio teknika desberdinak proposatuko ditugu datu-analisiak egiterakoan faltako balioei aurre egiteko aukera gisa. Hiru inputazio metodoren errendimendua aztertu dugu (K-Nearest Neighbor, Propensity Score eta Markov Chain Monte Carlo algoritmoak), faltako balioek datu multzo osoaren % 10a eta % 30a osatzen dutenean

Influence of Diagnostic Delay on Survival Rates for Patients with Colorectal Cancer

Colorectal cancer affects men and women alike. Sometimes, due to clinical-pathological factors, the absence of symptoms or the failure to conduct screening tests, its diagnosis may be delayed. However, it has not been conclusively shown that such a delay, especially when attributable to the health system, affects survival. The aim of the present study is to evaluate the overall survival rate of patients with a delayed diagnosis of colorectal cancer. This observational, prospective, multicenter study was conducted at 22 public hospitals located in nine Spanish provinces. For this analysis, 1688 patients with complete information in essential variables were included. The association between diagnostic delay and overall survival at five years, stratified according to tumor location, was estimated by the Kaplan-Meier method. Hazard ratios for this association were estimated using multivariable Cox regression models. The diagnostic delay ≥ 30 days was presented in 944 patients. The presence of a diagnostic delay of more than 30 days was not associated with a worse prognosis, contrary to a delay of less than 30 days (HR: 0.76, 0.64-0.90). In the multivariate analysis, a short delay maintained its predictive value (HR: 0.80, 0.66-0.98) regardless of age, BMI, Charlson index or TNM stage. A diagnostic delay of less than 30 days is an independent factor for short survival in patients with CRC. This association may arise because the clinical management of tumors with severe clinical characteristics and with a poorer prognosis are generally conducted more quickly.This study was supported by public grants from Instituto de Salud Carlos III (PI09/90397, PS09/00314, PS09/00746, PI09/90453, PI09/00910, PI09/90460, PI09/90490, PI13/01692, PI13/00013, PI18/01181, PI18/01589, PS0900805 & PI0900441) and was co-funded by the European Regional Development Fund.S

Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches

Real-life data are bounded and heavy-tailed variables. Zero-one-inflated beta (ZOIB) regression is used for modelling them. There are no appropriate methods to address the problem of missing data in repeated bounded outcomes. We developed an imputation method using ZOIB (i-ZOIB) and compared its performance with those of the naïve and machine-learning methods, using different distribution shapes and settings designed in the simulation study. The performance was measured employing the absolute error (MAE), root-mean-square-error (RMSE) and the unscaled mean bounded relative absolute error (UMBRAE) methods. The results varied depending on the missingness rate and mechanism. The i-ZOIB and the machine-learning ANN, SVR and RF methods showed the best performance

Inputazio tekniken errendimenduaren ebaluazioa bi neurketako luzeranzko datuetan

Neurketa errepikatuetan oinarrituriko behaketa-ikerketak menpeko aldagaien aldaketak denboran zehar aztertzeko erabiltzen dira. Bi neurketa baizik bakarrik egiten ez direnean, ikerketa helburu nagusietariko bat izan daiteke menpeko aldagaiaren batez besteko aldaketa aurresaten dituzten faktoreak zehaztea. Menpeko aldagaian faltako balioak ohikoak dira ikerketa mota hauetan, behaturiko datuen analisiaren emaitzak alboratuak gerta daitezkeelarik. Lan honetan inputazio teknika desberdinak proposatuko ditugu datu-analisiak egiterakoan faltako balioei aurre egiteko aukera gisa. Hiru inputazio metodoren errendimendua aztertu dugu (K-Nearest Neighbor, Propensity Score eta Markov Chain Monte Carlo algoritmoak), faltako balioek datu multzo osoaren % 10a eta % 30a osatzen dutenean

Incorporating PHI in decision making:external validation of the Rotterdam risk calculators for detection of prostate cancer

PURPOSE: External validation of existing risk calculators (RC) to assess the individualized risk of detecting prostate cancer (PCa) in prostate biopsies is needed to determine their clinical usefulness. The objective was to externally validate the Rotterdam Prostate Cancer RCs 3 and 4 (RPCRC-3/4) and that incorporating PHI (RPCRC-PHI) in a contemporary Spanish cohort. METHODS: Multicenter prospective study that included patients suspicious of harboring PCa. Men who attended the urology consultation were tested for PHI before prostate biopsy. To evaluate the performance of the prediction models: discrimination (receiver operating characteristic (ROC) curves), calibration and net benefit [decision curve analysis (DCA)] were calculated. These analyses were carried out for detection of any PCa and clinically significant (cs)PCa, defined as ISUP grade ≥ 2. RESULTS: Among the 559 men included, 337 (60.28%) and 194 (34.7%) were diagnosed of PCa and csPCa, respectively. RPCRC-PHI had the best discrimination ability for detection of PCa and csPCa with AUCs of 0.85 (95%CI 0.82-0.88) and 0.82 (95%CI 0.78-0.85), respectively. Calibration plots showed that RPCRC-3/4 underestimates the risk of detecting PCa showing the need for recalibration. In DCA, RPCRC-PHI shows the highest net benefit compared to biopsy all men. CONCLUSIONS: The RPCRC-PHI performed properly in a contemporary clinical setting, especially for prediction of csPCa.</p

Incorporating PHI in decision making:external validation of the Rotterdam risk calculators for detection of prostate cancer

PURPOSE: External validation of existing risk calculators (RC) to assess the individualized risk of detecting prostate cancer (PCa) in prostate biopsies is needed to determine their clinical usefulness. The objective was to externally validate the Rotterdam Prostate Cancer RCs 3 and 4 (RPCRC-3/4) and that incorporating PHI (RPCRC-PHI) in a contemporary Spanish cohort. METHODS: Multicenter prospective study that included patients suspicious of harboring PCa. Men who attended the urology consultation were tested for PHI before prostate biopsy. To evaluate the performance of the prediction models: discrimination (receiver operating characteristic (ROC) curves), calibration and net benefit [decision curve analysis (DCA)] were calculated. These analyses were carried out for detection of any PCa and clinically significant (cs)PCa, defined as ISUP grade ≥ 2. RESULTS: Among the 559 men included, 337 (60.28%) and 194 (34.7%) were diagnosed of PCa and csPCa, respectively. RPCRC-PHI had the best discrimination ability for detection of PCa and csPCa with AUCs of 0.85 (95%CI 0.82-0.88) and 0.82 (95%CI 0.78-0.85), respectively. Calibration plots showed that RPCRC-3/4 underestimates the risk of detecting PCa showing the need for recalibration. In DCA, RPCRC-PHI shows the highest net benefit compared to biopsy all men. CONCLUSIONS: The RPCRC-PHI performed properly in a contemporary clinical setting, especially for prediction of csPCa.</p

Development and Application of a Multi-Objective Tool for Thermal Design of Heat Exchangers Using Neural Networks

This paper presents the design of a multi-objective tool for sizing shell and tube heat exchangers (STHX), developed under a University/Industry collaboration. This work aims to show the feasibility of implementing artificial intelligence tools during the design of Heat Exchangers in industry. The design of STHX optimisation tools using artificial intelligence algorithms is a visited topic in the literature, nevertheless, the degree of implementation of this concept is uncommon in industrial companies. Thus, the challenge of this research consists of the development of a tool for the design of STHX using artificial intelligence algorithms that can be used by industrial companies. The approach is implemented using a simulated dataset contrasted with ARA TT, the company taking part in the project. The given dataset to develop a theoretical STHX calculator was modeled using MATLAB. This dataset was used to train seven neural networks (NNs). Three of them were mono-objective, one per objective to predict, and four were multi-objective. The last multi-objective NN was used to develop an inverse neural network (INN), which is used to find the optimal configuration of the STHXs. In this specific case, three design parameters, the pressure drop on the shell side, the pressure drop on the tube side and heat transfer rate, were jointly and successfully optimised. As a conclusion, this work proves that the developed tool is valid in both terms of effectiveness and user-friendliness for companies like ARA TT to improve their business activity