Investigation and analysis of patient outcomes, including in-hospital mortality and length of stay, are crucial for assisting clinicians in determining a patient's result at the outset of their hospitalization and for assisting hospitals in allocating their resources. This paper proposes an approach based on combining the well-known gray wolf algorithm with frequent items extracted by association rule mining algorithms. First, original features are combined with the discriminative extracted frequent items. The best subset of these features is then chosen, and the parameters of the used classification algorithms are also adjusted, using the gray wolf algorithm. This framework was evaluated using a real dataset made up of 2816 patients from the Imam Ali Kermanshah Hospital in Iran. The study's findings indicate that low Ejection Fraction, old age, high CPK values, and high Creatinine levels are the main contributors to patients' mortality. Several significant and interesting rules related to mortality in hospitals and length of stay have also been extracted and presented. Additionally, the accuracy, sensitivity, specificity, and auroc of the proposed framework for the diagnosis of mortality in the hospital using the SVM classifier were 0.9961, 0.9477, 0.9992, and 0.9734, respectively. According to the framework's findings, adding frequent items as features considerably improves classification accuracy.
翻译:对病人结果的研究和分析,包括住院死亡率和住院时间的调查与分析,对于协助临床医生在住院初期确定病人结果和帮助医院分配资源至关重要,本文件提出一种方法,将众所周知的灰色狼算法与关联规则采矿算法经常抽取的物品结合起来。首先,原有特征与歧视性抽取的常见物品相结合,然后选择了这些特征中最好的一组,还利用灰狼算法调整了所使用的分类算法参数。评价这一框架时使用了由伊朗伊玛目阿里·克尔曼沙哈医院的2816名病人组成的真实数据集。研究结果表明,低弹道、老年、高CPK值和高创造性水平是造成病人死亡的主要原因。还提取和介绍了与医院死亡率和住院时间长短有关的一些重要和有趣的规则。此外,利用SVM分类法分析仪分析医院死亡率的拟议框架的准确性、敏感性、具体性、具体性、以及奥罗克。使用SVM分类法诊断医院死亡率的拟议框架分别大大改进了0.991、0.992和0.94的准确性。