人工智能应用于北里奥格兰德州的床上监管:在"RegulaRN Leitos Gerais"平台上的数据分析和机器学习的应用
Tiago de Oliveira Barreto1, Fernando Lucas de Oliveira Farias1, Nicolas Vinícius Rodrigues Veras1,2
1Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil.
PloS one
|January 8, 2025
概括
机器学习模型可以优化巴西国家卫生系统 (SUS) 的医院病床监管. XGBoost,随机森林和梯度提升模型表现出高准确度和精度,改善了患者护理和床位可用性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 公共卫生系统 公共卫生系统
背景情况:
- 医院病床监管对于巴西国家卫生系统 (SUS) 中的患者护理管理至关重要.
- 里约格兰德多诺尔特州的RegulaRN Leitos Gerais平台最初管理了COVID-19床位请求,但扩展到包括各种条件.
- 优化床位分配对于减少患者等待时间和提高医疗保健效率至关重要.
研究的目的:
- 评估不同机器学习模型的性能,以预测医院病床规则的结果.
- 确定最有效的机器学习算法,以协助医疗监管机构做出决策.
- 在RegulaRN系统中提高床位分配的效率.
主要方法:
- 利用了RegulaRN数据库 (2021年10月至2024年1月) 拥有47,056条法规记录.
- 选择了12个特征,通过删除不完整的条目和不释放/死亡结果来预处理数据,并执行二进制分类.
- 应用并比较各种机器学习模型,包括XGBoost,随机森林,梯度增强和多层感知器与SGD优化器.
主要成果:
- XGBoost模型实现了最高的精度 (87.77%) 和回忆 (87.77%).
- 随机森林和梯度提升模型分别显示出更高的精度 (87.85%) 和F1-Score (87.56%).
- 带有SGD优化器的多层感知器记录了最佳的特异性 (82.94%) 和ROC-AUC (82.13%).
结论:
- 机器学习模型可以显著帮助医疗监管机构优化医院床位分配.
- 该研究确定了适用于提高床调节效率的特定模型 (XGBoost,随机森林,梯度提升,MLP).
- 有效的床位调节导致增加床位的可用性,并减少SUS内的患者等待时间.
相关概念视频
Classification of Signals
393
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
393
Multi-input and Multi-variable systems
96
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
96
Regression Analysis
5.6K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.6K
Classification of Systems-II
133
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
133
Classification of Systems-I
168
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
168
End Point Prediction: Gran Plot
272
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
272


