Karboub Kaouter, Tabaa Mohamed
FRDISI, Hassan II University Casablanca, Casablanca 20000, Morocco.
LRI-EAS, ENSEM, Hassan II University Casablanca, Casablanca 20000, Morocco.
Healthcare (Basel). 2022 May 24;10(6):966. doi: 10.3390/healthcare10060966.
This paper targets a major challenge of how to effectively allocate medical resources in intensive care units (ICUs). We trained multiple regression models using the Medical Information Mart for Intensive Care III (MIMIC III) database recorded in the period between 2001 and 2012. The training and validation dataset included pneumonia, sepsis, congestive heart failure, hypotension, chest pain, coronary artery disease, fever, respiratory failure, acute coronary syndrome, shortness of breath, seizure and transient ischemic attack, and aortic stenosis patients' recorded data. Then we tested the models on the unseen data of patients diagnosed with coronary artery disease, congestive heart failure or acute coronary syndrome. We included the admission characteristics, clinical prescriptions, physiological measurements, and discharge characteristics of those patients. We assessed the models' performance using mean residuals and running times as metrics. We ran multiple experiments to study the data partition's impact on the learning phase. The total running time of our best-evaluated model is 123,450.9 mS. The best model gives an average accuracy of 98%, highlighting the location of discharge, initial diagnosis, location of admission, drug therapy, length of stay and internal transfers as the most influencing patterns to decide a patient's readiness for discharge.
本文针对重症监护病房（ICU）中如何有效分配医疗资源这一重大挑战展开研究。我们使用2001年至2012年期间记录的重症监护医学信息集市三期（MIMIC III）数据库训练了多个回归模型。训练和验证数据集涵盖了肺炎、败血症、充血性心力衰竭、低血压、胸痛、冠状动脉疾病、发热、呼吸衰竭、急性冠状动脉综合征、呼吸急促、癫痫发作和短暂性脑缺血发作以及主动脉狭窄患者的记录数据。然后，我们在诊断为冠状动脉疾病、充血性心力衰竭或急性冠状动脉综合征患者的未见数据上对模型进行了测试。我们纳入了这些患者的入院特征、临床处方、生理测量数据和出院特征。我们使用平均残差和运行时间作为指标来评估模型的性能。我们进行了多次实验以研究数据划分对学习阶段的影响。我们评估的最佳模型的总运行时间为123,450.9毫秒。最佳模型的平均准确率为98%，突出了出院地点、初始诊断、入院地点、药物治疗、住院时间和内部转科是决定患者出院准备情况的最具影响力的模式。
