Neural Network-WPCA Based Method for Multi-Objective Optimal Redundancy Allocation

For systems with multiple redundancies, reliability evaluation in the redundancy allocation problem (RAP) constitutes a computational complexity. It has been demonstrated that neural network training provides an efficient approach to estimate the complex system reliability function. When executing the neural network algorithm, there are many parameters that need to be determined for improving the training performance. Therefore, robust experimental design method can be used to determine the neural network parameters. The traditional robust design methods are intended for a single response variable. However, the application of neural network method includes more than one measurement, such as estimation accuracy and time efficiency. In this paper, utility function is first estimated by neural network training, in which the algorithm parameters are determined by weighted principal component (WPCA)-based multi-response optimization which simultaneously optimizes more than one training performance measurements. Moreover, it is always desirable to simultaneously optimize several objectives in designing a system, such as reliability, cost, etc. Therefore, continuous WPCA-based multi-response design is then applied to obtain the best design of redundancies in RAP, which simultaneously optimize multiple objectives by taking into account the correlations between them. </jats:p

Cost Control of Treatment for Cerebrovascular Patients Using a Machine Learning Model in Western China

Background. Cerebrovascular disease has been the leading cause of death in China since 2017, and the control of medical expenses for these diseases is an urgent issue. Diagnosis-related groups (DRG) are increasingly being used to decrease the costs of healthcare worldwide. However, the classification variables and rules used vary from region to region. Of these variables, the question of whether the length of stay (LOS) should be used as a grouping variable is controversial. Aim. To identify the factors influencing inpatient medical expenditure in cerebrovascular disease patients. The performance of two sets of classification rules, and the effects of the extent of control of unreasonable medical treatment, were compared, to investigate whether the classification variables should include LOS. Methods. Data from 45,575 inpatients from a Healthcare Security Administration of a city in western China were used. Kruskal–Wallis H tests were used for single-factor analysis, and multiple linear stepwise regression was used to determine the main factors. A chi-squared automatic interaction detector (CHAID) algorithm was built as a decision tree model for grouping related data. The intensity of oversupply of service was controlled step by step from 10% to 100%, and the performance was calculated for each group. Results. The average hospitalization cost was 1,284 US dollars, and the total was 51.17 million US dollars. Of this, 43.42 million were paid by the government, and 7.75 million were paid by individuals. Factors including gender, age, type of insurance, level of hospital, LOS, surgery, therapeutic outcomes, main concomitant disease, and hypertension significantly influenced inpatient expenditure ( P &lt; 0.05 ). Incorporating LOS, the patients were divided into seven DRG groups, while without LOS, the patients were divided into eight DRG groups. More clinical variables were needed to achieve good results without LOS. Of the two rule sets, smaller coefficient of variation (CV) and a lower upper limit for patient costs were found in the group including LOS. Using this type of economic control, 3.35 million US dollars could be saved in one year.</jats:p

Risk factors associated with postoperative complications after liver cancer resection surgery in western China

Abstract Objectives Postoperative complications increase the workload of nursing staff as well as the financial and mental distress suffered by patients. The objective of this study is to identify clinical factors associated with postoperative complications after liver cancer resection surgery. Methods Data from liver cancer resections occurring between January 1st, 2019 to December 31st, 2019 was collected from the Department of Liver Surgery in West China Hospital of Sichuan University. The Kruskal–Wallis test and logistic regression were used to perform single-factor analysis. Stepwise logistic regression was used for multivariate analysis. Models were established using R 4.0.2 software. Results Based on data collected from 593 cases, the single-factor analysis determined that there were statistically significant differences in BMI, incision type, incision length, duration, incision range, and bleeding between cases that experienced complications within 30 days after surgery and those did not. Stepwise logistic regression models based on Kruskal–Wallis test and single-factor logistic regression determined that BMI, incision length, and duration were the primary factors causing complications after liver resection. The adjust OR of overweight patients and patients with obesity (stage 1) compared to low weight patients were 0.12 (95% CI:0.02–0.72) with p = 0.043 and 0.18 (95% CI:0.03–1.00) with p = 0.04, respectively. An increase of 1 cm in incision length increased the relative risk by 13%, while an increase of 10 min in surgical duration increased the relative risk by 15%. Conclusions The risk of postoperative complications after liver resection can be significantly reduced by controlling factors such as bleeding, incision length, and duration of the surgery. </jats:sec

Machine learning approaches for the prediction of postoperative complication risk in liver resection patients

Abstract Background For liver cancer patients, the occurrence of postoperative complications increases the difficulty of perioperative nursing, prolongs the hospitalization time of patients, and leads to large increases in hospitalization costs. The ability to identify influencing factors and to predict the risk of complications in patients with liver cancer after surgery could assist doctors to make better clinical decisions. Objective The aim of the study was to develop a postoperative complication risk prediction model based on machine learning algorithms, which utilizes variables obtained before or during the liver cancer surgery, to predict when complications present with clinical symptoms and the ways of reducing the risk of complications. Methods The study subjects were liver cancer patients who had undergone liver resection. There were 175 individuals, and 13 variables were recorded. 70% of the data were used for the training set, and 30% for the test set. The performance of five machine learning models, logistic regression, decision trees-C5.0, decision trees-CART, support vector machines, and random forests, for predicting postoperative complication risk in liver resection patients were compared. The significant influencing factors were selected by combining results of multiple methods, based on which the prediction model of postoperative complications risk was created. The results were analyzed to give suggestions of how to reduce the risk of complications. Results Random Forest gave the best performance from the decision curves analysis. The decision tree-C5.0 algorithm had the best performance of the five machine learning algorithms if ACC and AUC were used as evaluation indicators, producing an area under the receiver operating characteristic curve value of 0.91 (95% CI 0.77–1), with an accuracy of 92.45% (95% CI 85–100%), the sensitivity of 87.5%, and specificity of 94.59%. The duration of operation, patient’s BMI, and length of incision were significant influencing factors of postoperative complication risk in liver resection patients. Conclusions To reduce the risk of complications, it appears to be important that the patient's BMI should be above 22.96 before the operation, and the duration of the operation should be minimized. </jats:sec