AI-Enabled Clinical Decision Support System Modeling for the Prediction of Cirrhosis Complications

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2024-08-20 DOI:10.1016/j.irbm.2024.100854
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引用次数: 0

Abstract

Background and Objective

Utilizing artificial intelligence (AI), a clinical decision support system (CDSS), can help physicians anticipate possible complications of cirrhosis patients before prescribing more accurate treatments. This study aimed to establish a prototype of AI-CDSS modeling using electronic health records to predict five complications for cirrhosis patients who were controlled for oral antiviral drugs, lamivudine (LAM) or entecavir (ETV).

Methods

Our modeling attained a web-based AI-CDSS with four steps – data extraction, sample normalization, AI-enabled machine learning (ML), and system integration. We designed the extract-transform-load (ETL) procedure to filter the analytics features from a clinical database. The data training process applied 10-fold cross-validation to verify diverse ML models due to possible feature patterns with medications for predicting the complications. In addition, we applied both statistical means and standard deviations of the realistic datasets to create the simulative datasets, which contained sufficient and balanced data to train the most efficient models for evaluation. The modeling combined multiple ML methods, such as support vector machine (SVM), random forest (RF), extreme gradient boosting, naive Bayes, and logistic regression, for training fourteen features to generate the AI-CDSS's prediction functionality.

Results

The models achieving an accuracy of 0.8 after cross-validations would be qualified for the AI-CDSS. SVM and RF models using realistic data predicted jaundice with an accuracy of over 0.82. Furthermore, the SVM models using simulative data reached an accuracy of over 0.85 when predicting patients with jaundice. Our approaches implied that the simulative datasets based on the same distributions as that of the features in the realistic dataset were adequate for training the ML models. The RF model could reach an AUC of up to 0.82 for multiple complications by testing with the un-trained data. Finally, we successfully installed twenty models of the suitable ML methods in the AI-CDSS to predict five complications for cirrhosis patients prescribed with LAM or ETV.

Conclusions

Our modeling integrated a self-developed AI-CDSS with the approved ML models to predict cirrhosis complications for aiding clinical decision making.

Abstract Image

用于肝硬化并发症预测的人工智能临床决策支持系统建模
背景和目的利用人工智能(AI)--临床决策支持系统(CDSS),可以帮助医生在开出更准确的治疗处方之前预测肝硬化患者可能出现的并发症。本研究旨在利用电子健康记录建立人工智能-CDSS建模原型,以预测接受口服抗病毒药物拉米夫定(LAM)或恩替卡韦(ETV)治疗的肝硬化患者的五种并发症。方法我们的建模实现了基于网络的人工智能-CDSS,包括四个步骤--数据提取、样本规范化、人工智能机器学习(ML)和系统集成。我们设计了提取-转换-加载(ETL)程序,从临床数据库中筛选分析特征。在数据训练过程中,我们采用了 10 倍交叉验证,以验证因可能的特征模式而产生的多种 ML 模型,这些特征模式与预测并发症的药物有关。此外,我们还采用了现实数据集的统计均值和标准差来创建模拟数据集,其中包含足够且均衡的数据,以训练最有效的评估模型。建模结合了多种 ML 方法,如支持向量机 (SVM)、随机森林 (RF)、极梯度提升、天真贝叶斯和逻辑回归,用于训练 14 个特征,以生成 AI-CDSS 的预测功能。使用现实数据的 SVM 和 RF 模型预测黄疸的准确率超过了 0.82。此外,使用模拟数据的 SVM 模型预测黄疸患者的准确率超过了 0.85。我们的方法表明,基于与现实数据集特征相同分布的模拟数据集足以训练 ML 模型。通过使用未训练数据进行测试,射频模型对多种并发症的 AUC 可高达 0.82。最后,我们成功地在 AI-CDSS 中安装了 20 个合适的 ML 方法模型,用于预测肝硬化患者服用 LAM 或 ETV 后的五种并发症。
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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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