Department of Bioengineering, Clemson University, 301 Rhodes Research Center, Clemson, 29634, SC, USA.
School of Mathematical and Statistical Sciences, Clemson University, O-110 Martin Hall, Clemson, 29634, SC, USA.
Comput Methods Programs Biomed. 2023 Jun;236:107429. doi: 10.1016/j.cmpb.2023.107429. Epub 2023 Apr 18.
The quality of healthcare delivery depends directly on the skills of clinicians. For patients on hemodialysis, medical errors or injuries caused during cannulation can lead to adverse outcomes, including potential death. To promote objective skill assessment and effective training, we present a machine learning approach, which utilizes a highly-sensorized cannulation simulator and a set of objective process and outcome metrics.
In this study, 52 clinicians were recruited to perform a set of pre-defined cannulation tasks on the simulator. Based on data collected by sensors during their task performance, the feature space was then constructed based on force, motion, and infrared sensor data. Following this, three machine learning models- support vector machine (SVM), support vector regression (SVR), and elastic net (EN)- were constructed to relate the feature space to objective outcome metrics. Our models utilize classification based on the conventional skill classification labels as well as a new method that represents skill on a continuum.
With less than 5% of trials misplaced by two classes, the SVM model was effective in predicting skill based on the feature space. In addition, the SVR model effectively places both skill and outcome on a fine-grained continuum (versus discrete divisions) that is representative of reality. As importantly, the elastic net model enabled the identification of a set of process metrics that highly impact outcomes of the cannulation task, including smoothness of motion, needle angles, and pinch forces.
The proposed cannulation simulator, paired with machine learning assessment, demonstrates definite advantages over current cannulation training practices. The methods presented here can be adopted to drastically increase the effectiveness of skill assessment and training, thereby potentially improving clinical outcomes of hemodialysis treatment.
医疗服务质量直接取决于临床医生的技能。对于血液透析患者，置管过程中的医疗失误或损伤可能导致不良后果，甚至潜在死亡。为了促进客观技能评估和有效培训，我们提出了一种机器学习方法，该方法利用高度敏感的置管模拟器和一套客观的过程和结果指标。
本研究招募了 52 名临床医生在模拟器上执行一组预定义的置管任务。根据任务执行过程中传感器收集的数据，然后基于力、运动和红外传感器数据构建特征空间。之后，构建了三种机器学习模型——支持向量机（SVM）、支持向量回归（SVR）和弹性网络（EN），将特征空间与客观结果指标相关联。我们的模型既利用基于传统技能分类标签的分类，也利用代表技能连续体的新方法。
SVM 模型在基于特征空间预测技能方面非常有效，只有不到 5%的试验被两类错误分类。此外，SVR 模型能够在精细的连续体（而非离散分区）上有效地放置技能和结果，这与现实相符。同样重要的是，弹性网络模型能够识别出一组对置管任务结果有重大影响的过程指标，包括运动的平滑度、针的角度和夹力。
所提出的置管模拟器与机器学习评估相结合，与当前的置管培训实践相比具有明显优势。本文提出的方法可以极大地提高技能评估和培训的有效性，从而有可能改善血液透析治疗的临床结果。
