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首页> 外文期刊>BMC Medical Informatics and Decision Making >Patient similarity analytics for explainable clinical risk prediction
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Patient similarity analytics for explainable clinical risk prediction

机译：患者相似性分析解释可解释的临床风险预测

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Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility. Explainability is the extent of which a model’s prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model. The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n?=?7,041) and validated it on a testing dataset (n?=?3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process. The patient similarity model (AUROC?=?0.718) was comparable to the logistic regression (AUROC?=?0.695), RF (AUROC?=?0.764) and SVM models (AUROC?=?0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy. Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice.
机译：临床风险预测模型（CRPMS）使用患者特征来估计具有或开发特定疾病和/或结果的可能性。虽然CRPMS正在受欢迎，但他们尚未在临床实践中被广泛采用。缺乏可解释性和可解释性限制了其效用。解释性是可以描述模型的预测过程的程度。解释性是用户可以理解模型所做的预测的程度。该研究旨在证明患者相似性分析的效用，在开发可解释和可解释的CRPM时。在新加坡公共初级保健诊所的2型糖尿病患者的电子医疗记录中提取数据。我们使用了合并了专家输入的修改后邻居，在这个现实世界训练数据集（n？=？7,041）上开发患者相似性模型，并在测试数据集上验证它（n？= 3,018）。使用来自同一数据集的逻辑回归，随机森林（RF）和支持向量机（SVM）模型进行比较结果。然后在原型系统中实现患者相似性模型，以证明类似患者和预测过程的识别，解释性和可解释性。患者相似性模型（Auroc？= 0.718）与逻辑回归（Auroc？= 0.695），RF（Auroc？= 0.764）和SVM模型（Auroc？= 0.766）。我们在原型Web应用程序中打包了患者相似性模型。概念证明证明了应用程序如何以患者叙述的形式提供定量和定性信息。这些信息用于更好地通知和影响临床决策，例如让患者同意开始胰岛素治疗。患者相似性分析是一种可行的方法来开发可解释和可解释的CRPM。虽然该方法是概括的，但它可以用于根据其搜索的数据库开发本地相关信息。最终，这种方法可以产生更具信息丰富的CRPM，可以作为临床决策支持工具的一部分部署，以便在临床实践中更好地促进共享决策。

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