Buyer Julia, Oeser Alexander, Grieb Nora, Dietz Andreas, Neumuth Thomas, Stoehr Matthaeus
Innovation Center Computer Assisted Surgery (ICCAS), 04103 Leipzig, Germany.
Head and Neck Surgery, Department of Otolaryngology, University Hospital Leipzig, 04103 Leipzig, Germany.
Diagnostics (Basel). 2022 Apr 15;12(4):999. doi: 10.3390/diagnostics12040999.
Making complex medical decisions is becoming an increasingly challenging task due to the growing amount of available evidence to consider and the higher demand for personalized treatment and patient care. IT systems for the provision of clinical decision support (CDS) can provide sustainable relief if decisions are automatically evaluated and processed. In this paper, we propose an approach for quantifying similarity between new and previously recorded medical cases to enable significant knowledge transfer for reasoning tasks on a patient-level. Methodologically, 102 medical cases with oropharyngeal carcinoma were analyzed retrospectively. Based on independent disease characteristics, patient-specific data vectors including relevant information entities for primary and adjuvant treatment decisions were created. Utilizing the correlation coefficient as the methodological foundation of our approach, we were able to determine the predictive impact of each characteristic, thus enabling significant reduction of the feature space to allow for further analysis of the intra-variable distances between the respective feature states. The results revealed a significant feature-space reduction from initially 19 down to only 6 diagnostic variables ( correlation coefficient ≥ 0.3, significance test ≥ 2.5) for the primary and 7 variables (from initially 14) for the adjuvant treatment setting. Further investigation on the resulting characteristics showed a non-linear behavior in relation to the corresponding distances on intra-variable level. Through the implementation of a 10-fold cross-validation procedure, we were further able to identify 8 (primary treatment) matching cases with an evaluation score of 1.0 and 9 (adjuvant treatment) matching cases with an evaluation score of 0.957 based on their shared treatment procedure as the endpoint for similarity definition. Based on those promising results, we conclude that our proposed method for using data-driven similarity measures for application in medical decision-making is able to offer valuable assistance for physicians. Furthermore, we consider our approach as universal in regard to other clinical use-cases, which would allow for an easy-to-implement adaptation for a range of further medical decision-making scenarios.
由于需要考虑的现有证据越来越多，以及对个性化治疗和患者护理的更高要求，做出复杂的医疗决策正变得越来越具有挑战性。如果决策能够自动评估和处理，那么提供临床决策支持（CDS）的信息技术系统可以提供可持续的缓解。在本文中，我们提出了一种方法，用于量化新的和先前记录的医疗病例之间的相似度，以便在患者层面进行推理任务时实现重要的知识转移。从方法学上讲，我们回顾性分析了102例口咽癌医疗病例。基于独立的疾病特征，创建了患者特定的数据向量，其中包括用于主要和辅助治疗决策的相关信息实体。利用相关系数作为我们方法的方法论基础，我们能够确定每个特征的预测影响，从而显著减少特征空间，以便进一步分析各个特征状态之间的变量内距离。结果显示，主要治疗的特征空间从最初的19个显著减少到仅6个诊断变量（相关系数≥0.3，显著性检验≥2.5），辅助治疗设置的特征空间从最初的14个减少到7个变量。对所得特征的进一步研究表明，在变量内层面，其与相应距离存在非线性行为。通过实施10倍交叉验证程序，我们进一步能够根据共享治疗程序作为相似度定义的终点，识别出8例（主要治疗）匹配病例，评估分数为1.0，以及9例（辅助治疗）匹配病例，评估分数为0.957。基于这些有前景的结果，我们得出结论，我们提出的用于医疗决策的数据驱动相似度测量方法能够为医生提供有价值的帮助。此外，我们认为我们的方法在其他临床用例方面具有通用性，这将允许轻松实现对一系列进一步医疗决策场景的适应。
