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首页> 外文期刊>Knowledge-Based Systems >Predicting ICD-9 code groups with fuzzy similarity based supervised multi-label classification of unstructured clinical nursing notes
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Predicting ICD-9 code groups with fuzzy similarity based supervised multi-label classification of unstructured clinical nursing notes

机译：基于模糊相似度的非结构化临床护理笔记的监督多标签分类预测ICD-9代码组

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摘要

In hospitals, caregivers are trained to chronicle the subtle changes in the clinical conditions of a patient at regular intervals, for enabling decision-making. Caregivers text-based clinical notes are a significant source of rich patient-specific data, that can facilitate effective clinical decision support, despite which, this treasure-trove of data remains largely unexplored for supporting the prediction of clinical outcomes. The application of sophisticated data modeling and prediction algorithms with greater computational capacity have made disease prediction from raw clinical notes a relevant problem. In this paper, we propose an approach based on vector space and topic modeling, to structure the raw clinical data by capturing the semantic information in the nursing notes. Fuzzy similarity based data cleansing approach was used to merge anomalous and redundant patient data. Furthermore, we utilize eight supervised multi-label classification models to facilitate disease (ICD-9 code group) prediction. We present an exhaustive comparative study to evaluate the performance of the proposed approaches using standard evaluation metrics. Experimental validation on MIMIC-III, an open database, underscored the superior performance of the proposed Term weighting of unstructured notes AGgregated using fuzzy Similarity (TAGS) model, which consistently outperformed the state-of-the-art structured data based approach by 7.79% in AUPRC and 1.24% in AUROC. (C) 2019 Elsevier B.V. All rights reserved.
机译：在医院中，训练有素的护理人员定期记录患者临床状况的细微变化，以便做出决策。照护者基于文本的临床笔记是丰富的患者特定数据的重要来源，可以促进有效的临床决策支持，尽管如此，仍未充分利用此数据宝库来支持临床结果的预测。具有更高计算能力的复杂数据建模和预测算法的应用已使根据原始临床记录进行疾病预测成为一个相关问题。在本文中，我们提出了一种基于向量空间和主题建模的方法，通过捕获护理笔记中的语义信息来构造原始临床数据。基于模糊相似度的数据清理方法用于合并异常和冗余的患者数据。此外，我们利用八个监督的多标签分类模型来促进疾病（ICD-9代码组）的预测。我们目前进行了详尽的比较研究，以使用标准评估指标评估提议方法的性能。在开放式数据库MIMIC-III上进行的实验验证，强调了所提出的使用模糊相似度（TAGS）模型聚合的非结构化票据的期限加权的优越性能，该性能始终比基于结构化数据的最新方法高7.79％在AUPRC中占1.24％。 （C）2019 Elsevier B.V.保留所有权利。

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