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An AI Approach for Identifying Patients With Cirrhosis
Journal of Clinical Gastroenterology ( IF 2.9 ) Pub Date : 2023-01-01 , DOI: 10.1097/mcg.0000000000001586
Jihad S Obeid 1 , Ali Khalifa 2 , Brandon Xavier 2 , Halim Bou-Daher 2 , Don C Rockey 2, 3
Affiliation  

Goal: 

The goal of this study was to evaluate an artificial intelligence approach, namely deep learning, on clinical text in electronic health records (EHRs) to identify patients with cirrhosis.

Background and Aims: 

Accurate identification of cirrhosis in EHR is important for epidemiological, health services, and outcomes research. Currently, such efforts depend on International Classification of Diseases (ICD) codes, with limited success.

Materials and Methods: 

We trained several machine learning models using discharge summaries from patients with known cirrhosis from a patient registry and random controls without cirrhosis or its complications based on ICD codes. Models were validated on patients for whom discharge summaries were manually reviewed and used as the gold standard test set. We tested Naive Bayes and Random Forest as baseline models and a deep learning model using word embedding and a convolutional neural network (CNN).

Results: 

The training set included 446 cirrhosis patients and 689 controls, while the gold standard test set included 139 cirrhosis patients and 152 controls. Among the machine learning models, the CNN achieved the highest area under the receiver operating characteristic curve (0.993), with a precision of 0.965 and recall of 0.978, compared with 0.879 and 0.981 for the Naive Bayes and Random Forest, respectively (precision 0.787 and 0.958, and recalls 0.878 and 0.827). The precision by ICD codes for cirrhosis was 0.883 and recall was 0.978.

Conclusions: 

A CNN model trained on discharge summaries identified cirrhosis patients with high precision and recall. This approach for phenotyping cirrhosis in the EHR may provide a more accurate assessment of disease burden in a variety of studies.



中文翻译:

一种识别肝硬化患者的人工智能方法

目标: 

本研究的目的是评估一种人工智能方法,即深度学习,利用电子健康记录 (EHR) 中的临床文本来识别肝硬化患者。

背景和目标: 

准确识别EHR中的肝硬化对于流行病学、卫生服务和结果研究非常重要。目前,此类努力依赖于国际疾病分类(ICD) 代码,但收效甚微。

材料和方法: 

我们使用来自患者登记处的已知肝硬化患者的出院摘要和基于 ICD 代码的无肝硬化或其并发症的随机对照训练了多个机器学习模型。模型在患者身上进行了验证,这些患者的出院摘要被人工审查并用作金标准测试集。我们测试了朴素贝叶斯和随机森林作为基线模型和使用词嵌入和卷积神经网络 (CNN) 的深度学习模型。

结果: 

训练集包括 446 名肝硬化患者和 689 名对照,而金标准测试集包括 139 名肝硬化患者和 152 名对照。在机器学习模型中,CNN 的受试者工作特征曲线下面积最高(0.993),精度为 0.965,召回率为 0.978,而朴素贝叶斯和随机森林分别为 0.879 和 0.981(精度为 0.787 和0.958,并回忆起 0.878 和 0.827)。ICD 代码对肝硬化的精确度为 0.883,召回率为 0.978。

结论: 

在出院摘要上训练的 CNN 模型以高精度和召回率识别出肝硬化患者。这种在EHR中对肝硬化进行表型分型的方法可以在各种研究中更准确地评估疾病负担。

更新日期:2022-12-12
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