Setiawan Devin, Wiranto Yumiko, Girard Jeffrey M, Watts Amber, Ashourvan Arian
The University of Kansas, Department of Electrical Engineering and Computer Science, 1415 Jayhawk Blvd. Lawrence, KS 66045.
The University of Kansas, Department of Psychology, 1415 Jayhawk Blvd. Lawrence, KS 66045.
medRxiv. 2024 Dec 7:2024.07.24.24310941. doi: 10.1101/2024.07.24.24310941.
Traditional clinical assessments often lack individualization, relying on standardized procedures that may not accommodate the diverse needs of patients, especially in early stages where personalized diagnosis could offer significant benefits. We aim to provide a machine-learning framework that addresses the individualized feature addition problem and enhances diagnostic accuracy for clinical assessments.
Individualized Clinical Assessment Recommendation System (iCARE) employs locally weighted logistic regression and Shapley Additive Explanations (SHAP) value analysis to tailor feature selection to individual patient characteristics. Evaluations were conducted on synthetic and real-world datasets, including early-stage diabetes risk prediction and heart failure clinical records from the UCI Machine Learning Repository. We compared the performance of iCARE with a Global approach using statistical analysis on accuracy and area under the ROC curve (AUC) to select the best additional features.
The iCARE framework enhances predictive accuracy and AUC metrics when additional features exhibit distinct predictive capabilities, as evidenced by synthetic datasets 1-3 and the early diabetes dataset. Specifically, in synthetic dataset 1, iCARE achieved an accuracy of 0·999 and an AUC of 1·000, outperforming the Global approach with an accuracy of 0·689 and an AUC of 0·639. In the early diabetes dataset, iCARE shows improvements of 1·5-3·5% in accuracy and AUC across different numbers of initial features. Conversely, in synthetic datasets 4-5 and the heart failure dataset, where features lack discernible predictive distinctions, iCARE shows no significant advantage over global approaches on accuracy and AUC metrics.
iCARE provides personalized feature recommendations that enhance diagnostic accuracy in scenarios where individualized approaches are critical, improving the precision and effectiveness of medical diagnoses.
This work was supported by startup funding from the Department of Psychology at the University of Kansas provided to A.A., and the R01MH125740 award from NIH partially supported J.M.G.'s work.
传统的临床评估往往缺乏个性化，依赖标准化程序，可能无法满足患者的多样化需求，尤其是在早期阶段，个性化诊断可能带来显著益处。我们旨在提供一个机器学习框架，以解决个性化特征添加问题，并提高临床评估的诊断准确性。
个性化临床评估推荐系统（iCARE）采用局部加权逻辑回归和夏普利值（SHAP）分析，根据个体患者特征定制特征选择。在合成数据集和真实世界数据集上进行评估，包括来自UCI机器学习库的早期糖尿病风险预测和心力衰竭临床记录。我们使用统计分析比较了iCARE与全局方法在准确性和ROC曲线下面积（AUC）方面的性能，以选择最佳的附加特征。
当附加特征具有明显的预测能力时，iCARE框架提高了预测准确性和AUC指标，合成数据集1-3和早期糖尿病数据集证明了这一点。具体而言，在合成数据集1中，iCARE的准确率达到0.999，AUC为达到1.000，优于全局方法，全局方法的准确率为0.689，AUC为0.639。在早期糖尿病数据集中，iCARE在不同数量的初始特征下，准确率和AUC提高了1.5%-至3.5%。相反，在合成数据集4-5和心力衰竭数据集中，特征缺乏明显的预测差异，iCARE在准确性和AUC指标上与全局方法相比没有显著优势。
iCARE提供个性化特征推荐，在个性化方法至关重要的情况下提高诊断准确性，提高医学诊断的精度和有效性。
这项工作得到了堪萨斯大学心理学系提供给A.A.的启动资金支持，美国国立卫生研究院（NIH）的R01MH125740奖部分支持了J.M.G.的工作。
