Department of Health Informatics, College of Public Health and Health Informatics, King Saud Ibn Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia.
King Abdullah International Medical Research Center, Riyadh 14611, Saudi Arabia.
Medicina (Kaunas). 2023 Jul 9;59(7):1276. doi: 10.3390/medicina59071276.
Early detection of sepsis is crucial and can save lives. However, identifying sepsis early and accurately remains a difficult task in the medical field. This study aims to investigate a new machine-learning approach. By analyzing the clinical laboratory results and vital signs of adult patients in the ICU, this approach can predict and detect the initial signs of sepsis. To examine survival rates and predict outcomes, the study utilized several models, including the proportional hazards model and data mining algorithms. We analyzed data from the BESTCare database at KAMC, with a focus on patients aged 14 and older who were admitted to the ICU between April and October 2018. We conducted a thorough analysis of the medical records of a total of 1182 patients who were diagnosed with sepsis. We studied two approaches to predict sepsis in ICU patients. The regression model utilizing survival analysis showed moderate predictive ability, emphasizing the importance of only three factors-time (from sepsis to an outcome; discharge or death), lactic acid, and temperature-had a significant -value ( = 0.000568, = 0.01, = 0.02, respectively). Other data mining algorithms may have limitations due to their assumptions of variable independence and linear classification nature. To achieve progress and accuracy in the field of sepsis prediction, it is important to continuously strive for improvement. By meticulously cleaning and selecting data attributes, we can create a strong foundation for future advancements in this area.
早期发现脓毒症至关重要，可以挽救生命。然而，在医学领域，早期准确地识别脓毒症仍然是一项艰巨的任务。本研究旨在探讨一种新的机器学习方法。通过分析 ICU 中成年患者的临床实验室结果和生命体征，该方法可以预测和检测脓毒症的初始迹象。为了检查生存率并预测结果，该研究使用了几种模型，包括比例风险模型和数据挖掘算法。我们分析了 KAMC 的 BESTCare 数据库的数据，重点关注 2018 年 4 月至 10 月期间入住 ICU 的 14 岁及以上患者。我们对总共 1182 名被诊断为脓毒症的患者的病历进行了彻底分析。我们研究了两种预测 ICU 患者脓毒症的方法。利用生存分析的回归模型显示出中等的预测能力，强调了仅考虑三个因素（从脓毒症到结局；出院或死亡）的重要性——时间、乳酸和温度——具有显著的 -值（= 0.000568，= 0.01，= 0.02，分别）。其他数据挖掘算法可能由于其对变量独立性和线性分类性质的假设而存在局限性。为了在脓毒症预测领域取得进展和准确性，不断努力改进非常重要。通过精心清理和选择数据属性，我们可以为该领域的未来发展奠定坚实的基础。
