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In dealing with real world transportation problems, several issues are frequently encountered as a result of uncontrollable factors. To deal this uncertainties, many authors have suggested transportation problems with intuitionistic fuzzy parameters. In this article, fully intuitionistic fuzzy transportation problem (InFTP) is considered in which the parameters are triangular intuitionistic fuzzy numbers. To solve this, initially intuitionistic fuzzy branch and bound technique is applied to get the initial basic feasible solution and then intuitionistic fuzzy modified distribution method is applied to acquire the optimal solution of the fully InFTP. Also a new ordering is developed here in which the compensation, linearity and additive property is satisfied. The optimal solution obtained from the proposed method satisfies the condition of optimality and feasibility. Finally two numerical examples are provided to show the effectiveness of the proposed method. From the comparative analysis, the proposed approach shows better optimal solution than existing methods.

Mortality prediction in surgical intensive care units (SICUs) is considered to be among the most critical steps in enforcing efficient treatment policies. This study aims to evaluate the performance of various deep learning models in predicting the mortality of patients admitted to SICUs. The survival of 2,225 adult patients admitted to SICUs was modeled using five salient deep learning-based survival models, namely, Cox-CC, Cox-Time, DeepSurv, DeepHit, and N-MTLR. The data were extracted from the Medical Information Mart for Intensive Care II (MIMIC-II) database. The performance of the models was compared using the time-dependent concordance index (Ctd-index) and integrated Brier score (IBS). From among the five models, DeepSurv achieved the most accurate prediction, while Cox-Time demonstrated the least optimal predictive ability. For DeepSurv, Cox-CC, DeepHit, N-MTLR, and Cox-Time, the mean Ctd -index was 0.773, 0.767, 0.765, 0.732, and 0.659, and the mean IBS was 0.181, 0.192, 0.195, 0.212, and 0.225, respectively. DeepSurv, Cox-CC, and DeepHit yielded comparable performance. Deep learning models are free from the stringent assumptions inherent in standard survival models. Hence, these models are considered flexible alternatives to the standard approaches in scalable, real-world survival problems.

Introduction: The relationship between occurrence of sepsis and Body Mass Index (BMI) has been studied by treating BMI as a categorical variable based on the standard categories developed by the World Health Organisation (WHO). This technique masks the actual form of the relationship that exist between BMI and sepsis. Aim: To determine the shape of the association between BMI and probability of sepsis after adjusting the effect of gender, age, ethnicity and co-morbidities among critically-ill patients and to identify an appropriate technique to elucidate this association. Materials and Methods: The study was based on the data extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database, a real-world dataset. A robust Poisson regression model and a Restricted Cubic Spline (RCS) regression model were applied to model the data. Results: The study revealed a non linear relationship between BMI and occurrence of sepsis. From the RCS plot, an increased risk of sepsis was observed among underweight and obese males and a similar pattern was observed among those with hypertension. BMI, age, and hypertension were found to be significant in the robust Poisson model (RR 1.01, 95% CI 1.00-1.02, RR 1.01, 95% CI 1.00- 1.02, and RR 0.76, 95% CI 0.69-0.84, p<0.05, respectively). Conclusion: A non linear relationship was observed between BMI and occurrence of sepsis after adjusting for other factors in the critically-ill patients. Female patients in both underweight and obese category had lower occurrence of sepsis as compared to those with normal BMI. It is also worth noting that the status of diabetes did not display any association with BMI.