使用机器学习识别可预防的医院急诊
Sarah A Alkhodair1, Norah Altwaijri1, Ahmed I Albarrak2
1IT Department, CCIS, King Saud University, Riyadh, Saudi Arabia.
机器学习模型可以帮助急诊室识别紧急病例,减少等待时间,通过过不必要的访问来改善关键病人的护理.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 公共卫生 公共卫生
背景情况:
- 紧急服务部门 (ED) 过度拥挤是全球卫生挑战.
- 增加非紧急问题的ED访问加剧了过度拥挤,导致更长的等待时间,更高的死亡率和急性疾病的延迟护理.
研究的目的:
- 开发和评估机器学习 (ML) 模型,以区分紧急和非紧急ED访问.
- 优化资源配置,改善紧急护理机构患者的治疗结果.
主要方法:
- 实施四个ML模型:决策树,随机森林,AdaBoost和XGBoost.
- 使用现实世界ED数据对模型性能进行评估.
主要成果:
- 与其他模型相比,XGBoost模型表现出优越的性能.
- 在区分紧急和非紧急ED访问方面,XGBoost获得了最高的准确性和F1得分.
结论:
- 机器学习,特别是XGBoost,在有效管理ED过度拥挤方面显示出巨大潜力.
- 准确区分患者需求可以提高急诊室的效率和患者护理.
更多相关视频
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
相关概念视频
Documentation of Nursing Diagnosis
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
Steps in Outbreak Investigation
Healthcare Associated Infections II: Preventive Measures
The best practices for preventing healthcare-associated infections include hand hygiene, patient risk...
Errors occurring during blood pressure monitoring
Several factors...
Acute Kidney Injury IV: Diagnostic Studies and Prevention
Issues And Trends In Healthcare Delivery System
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
