Shaoze Cui1, Dujuan Wang2, Yanzhang Wang1, Pay-Wen Yu3, Yaochu Jin4. 1. School of Management Science and Engineering, Dalian University of Technology, Dalian 116023, PR China. 2. Business School of Sichuan University, Chengdu 610064, China. Electronic address: wangdujuan@dlut.edu.cn. 3. Department of Physical Education, Fu Jen Catholic University, New Taipei City 24205, Taiwan. 4. School of Management Science and Engineering, Dalian University of Technology, Dalian 116023, PR China; Department of Computer Science, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom.
Abstract
BACKGROUND AND OBJECTIVE: In healthcare systems, the cost of unplanned readmission accounts for a large proportion of total hospital payment. Hospital-specific readmission rate becomes a critical issue around the world. Quantification and early identification of unplanned readmission risks will improve the quality of care during hospitalization and reduce the occurrence of readmission. In clinical practice, medical workers generally use LACE score method to evaluate patient readmission risks, but this method usually performs poorly. With this in mind, this study presents a novel method combining support vector machine and genetic algorithm to build the risk prediction model, which simultaneously involves feature selection and the processing of imbalanced data. This model aims to provide decision support for clinicians during the discharge management of patients with diabetes. METHOD: The experiments were conducted from a set of 8756 medical records with 50 different features about diabetic readmission. After preprocessing the data, an effective SMOTE-based method was proposed to solve the imbalance data problem. Further, in order to improve prediction performance, a hybrid feature selection mechanism was devised to select the important features. Subsequently, an improved support vector machine-based (SVM-based) method was developed and the genetic algorithm was used to tune the sensitive parameter of the algorithm. Finally, the five-fold cross-validation method was applied to compare the performance of proposed method with other methods (LACE score, logistic regression, naïve bayes, decision tree and feed forward neural networks). RESULTS: Experimental results indicate that the proposed SVM-based method achieves an accuracy of 81.02%, a sensitivity of 82.89%, a specificity of 79.23%, and outperforms other popular algorithms in identifying diabetic patients who may be readmitted. CONCLUSIONS: Our research can improve the performance of clinic decision support systems for diabetic readmission, by which the readmission possibility as well as the waste of medical resources can be reduced.
BACKGROUND AND OBJECTIVE: In healthcare systems, the cost of unplanned readmission accounts for a large proportion of total hospital payment. Hospital-specific readmission rate becomes a critical issue around the world. Quantification and early identification of unplanned readmission risks will improve the quality of care during hospitalization and reduce the occurrence of readmission. In clinical practice, medical workers generally use LACE score method to evaluate patient readmission risks, but this method usually performs poorly. With this in mind, this study presents a novel method combining support vector machine and genetic algorithm to build the risk prediction model, which simultaneously involves feature selection and the processing of imbalanced data. This model aims to provide decision support for clinicians during the discharge management of patients with diabetes. METHOD: The experiments were conducted from a set of 8756 medical records with 50 different features about diabetic readmission. After preprocessing the data, an effective SMOTE-based method was proposed to solve the imbalance data problem. Further, in order to improve prediction performance, a hybrid feature selection mechanism was devised to select the important features. Subsequently, an improved support vector machine-based (SVM-based) method was developed and the genetic algorithm was used to tune the sensitive parameter of the algorithm. Finally, the five-fold cross-validation method was applied to compare the performance of proposed method with other methods (LACE score, logistic regression, naïve bayes, decision tree and feed forward neural networks). RESULTS: Experimental results indicate that the proposed SVM-based method achieves an accuracy of 81.02%, a sensitivity of 82.89%, a specificity of 79.23%, and outperforms other popular algorithms in identifying diabeticpatients who may be readmitted. CONCLUSIONS: Our research can improve the performance of clinic decision support systems for diabetic readmission, by which the readmission possibility as well as the waste of medical resources can be reduced.
Authors: Jessica M Schwartz; Amanda J Moy; Sarah C Rossetti; Noémie Elhadad; Kenrick D Cato Journal: J Am Med Inform Assoc Date: 2021-03-01 Impact factor: 4.497
Authors: Girish Dwivedi; Frank M Sanfilippo; Juan Lu; Ling Wang; Mohammed Bennamoun; Isaac Ward; Senjian An; Ferdous Sohel; Benjamin J W Chow Journal: Sci Rep Date: 2021-09-15 Impact factor: 4.379