Rosaly Moreno Mendez , Antonio Marín , José Ramon Ferrando , Giuliana Rissi Castro , Sonia Cepeda Madrigal , Gabriela Agostini , Pablo Catalan Serra
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Based on an exploratory data analysis (EDA), independent variables (according to mutual information analysis) were trained using a gradient boosting algorithm (GBT) and validated through cross-validation.</p></div><div><h3>Results</h3><p>With an area under the curve close to unity, the model predicted a spirometric obstructive pattern using variables with the highest predictive power: FEV1_theoretical_pre values. Sensitivity: 93%. Positive predictive value: 94%. Specificity: 97%. Negative predictive value: 96%. Accuracy: 95%. Precision: 94%.</p></div><div><h3>Conclusion</h3><p>An ML model can predict the presence of an obstructive pattern in spirometry in a primary care smoking population with no prior diagnosis of respiratory disease using the FEV1_theoretical_pre values with an accuracy and precision exceeding 90%. Further studies including clinical data and strategies for integrating AI into clinical workflow are needed.</p></div>","PeriodicalId":34317,"journal":{"name":"Open Respiratory Archives","volume":"6 ","pages":"Article 100313"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S265966362400016X/pdfft?md5=48496b70dbeeabf981ea1982451c3307&pid=1-s2.0-S265966362400016X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence Applied to Forced Spirometry in Primary Care\",\"authors\":\"Rosaly Moreno Mendez , Antonio Marín , José Ramon Ferrando , Giuliana Rissi Castro , Sonia Cepeda Madrigal , Gabriela Agostini , Pablo Catalan Serra\",\"doi\":\"10.1016/j.opresp.2024.100313\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><p>This study aims to create an artificial intelligence (AI) based machine learning (ML) model capable of predicting a spirometric obstructive pattern using variables with the highest predictive power derived from an active case-finding program for COPD in primary care.</p></div><div><h3>Material and methods</h3><p>A total of 1190 smokers, aged 30–80 years old with no prior history of respiratory disease, underwent spirometry with bronchodilation. 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引用次数: 0
摘要
导言:本研究旨在创建一个基于人工智能(AI)的机器学习(ML)模型,该模型能够利用从基层医疗机构慢性阻塞性肺疾病(COPD)积极病例调查项目中获得的具有最高预测能力的变量来预测肺功能阻塞模式。使用人工智能工具对样本进行了分析。在探索性数据分析(EDA)的基础上,使用梯度提升算法(GBT)对自变量(根据互信息分析)进行了训练,并通过交叉验证进行了验证。结果该模型使用预测能力最强的变量预测了肺活量阻塞模式,曲线下面积接近统一:FEV1_theoretical_pre 值。灵敏度:93%。阳性预测值:94%。特异性:97%。阴性预测值:96%。准确率:95%。结论:一个 ML 模型可以使用 FEV1_theoretical_pre 值预测既往未确诊呼吸系统疾病的初级保健吸烟人群肺活量测量中是否存在阻塞模式,准确率和精确率均超过 90%。还需要进一步研究临床数据以及将人工智能融入临床工作流程的策略。
Artificial Intelligence Applied to Forced Spirometry in Primary Care
Introduction
This study aims to create an artificial intelligence (AI) based machine learning (ML) model capable of predicting a spirometric obstructive pattern using variables with the highest predictive power derived from an active case-finding program for COPD in primary care.
Material and methods
A total of 1190 smokers, aged 30–80 years old with no prior history of respiratory disease, underwent spirometry with bronchodilation. The sample was analyzed using AI tools. Based on an exploratory data analysis (EDA), independent variables (according to mutual information analysis) were trained using a gradient boosting algorithm (GBT) and validated through cross-validation.
Results
With an area under the curve close to unity, the model predicted a spirometric obstructive pattern using variables with the highest predictive power: FEV1_theoretical_pre values. Sensitivity: 93%. Positive predictive value: 94%. Specificity: 97%. Negative predictive value: 96%. Accuracy: 95%. Precision: 94%.
Conclusion
An ML model can predict the presence of an obstructive pattern in spirometry in a primary care smoking population with no prior diagnosis of respiratory disease using the FEV1_theoretical_pre values with an accuracy and precision exceeding 90%. Further studies including clinical data and strategies for integrating AI into clinical workflow are needed.