Haomin Li , Siyuan Gao , Dan Wu , Min Zhu , Zhenzhen Hu , Kexin Fang , Xiuru Chen , Zhou Ni , Jing Li , Beibei Zhao , Xuhui She , Xinwen Huang
{"title":"训练机器学习模型以检测基于GC-MS尿液代谢组学的罕见先天性代谢错误(IEMs),用于疾病筛查。","authors":"Haomin Li , Siyuan Gao , Dan Wu , Min Zhu , Zhenzhen Hu , Kexin Fang , Xiuru Chen , Zhou Ni , Jing Li , Beibei Zhao , Xuhui She , Xinwen Huang","doi":"10.1016/j.ijmedinf.2024.105765","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Gas chromatography-mass spectrometry (GC–MS) has been shown to be a potentially efficient metabolic profiling platform in urine analysis. However, the widespread use of GC–MS for inborn errors of metabolism (IEM) screening is constrained by the rarity of IEM in population, and the difficult and specialized complexity of the interpretation of GC–MS organic acid profiles.</div></div><div><h3>Methods</h3><div>Based on 355,197 GC–MS test cases accumulated from 2013 to 2021 in China, a random forest-based machine learning model was proposed, trained, and evaluated. Weighted undersampling or oversampling data processing and staged modeling strategies were used to handle the highly imbalanced data and improve the ability of the model to identify different types of rare IEM cases.</div></div><div><h3>Result</h3><div>In the first-stage model, which only identified positive cases without discriminating the specific IEM, the screening sensitivity was 0.938 (or 0.991 if abnormal cases were also included). The average sensitivity of the second-stage models that classify 11 particular IEMs is 0.992, with an average specificity and accuracy of 0.944 and 0.969, respectively. The SHAP values visualized for each model explain the basis for the differential diagnosis made by the model.</div></div><div><h3>Conclusion</h3><div>With sufficient high-quality data, machine learning models can provide high-sensitivity GC–MS interpretation and greatly improve the efficiency and quality of GC–MS based IEM screening.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105765"},"PeriodicalIF":3.7000,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Training machine learning models to detect rare inborn errors of metabolism (IEMs) based on GC–MS urinary metabolomics for diseases screening\",\"authors\":\"Haomin Li , Siyuan Gao , Dan Wu , Min Zhu , Zhenzhen Hu , Kexin Fang , Xiuru Chen , Zhou Ni , Jing Li , Beibei Zhao , Xuhui She , Xinwen Huang\",\"doi\":\"10.1016/j.ijmedinf.2024.105765\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Gas chromatography-mass spectrometry (GC–MS) has been shown to be a potentially efficient metabolic profiling platform in urine analysis. However, the widespread use of GC–MS for inborn errors of metabolism (IEM) screening is constrained by the rarity of IEM in population, and the difficult and specialized complexity of the interpretation of GC–MS organic acid profiles.</div></div><div><h3>Methods</h3><div>Based on 355,197 GC–MS test cases accumulated from 2013 to 2021 in China, a random forest-based machine learning model was proposed, trained, and evaluated. Weighted undersampling or oversampling data processing and staged modeling strategies were used to handle the highly imbalanced data and improve the ability of the model to identify different types of rare IEM cases.</div></div><div><h3>Result</h3><div>In the first-stage model, which only identified positive cases without discriminating the specific IEM, the screening sensitivity was 0.938 (or 0.991 if abnormal cases were also included). The average sensitivity of the second-stage models that classify 11 particular IEMs is 0.992, with an average specificity and accuracy of 0.944 and 0.969, respectively. The SHAP values visualized for each model explain the basis for the differential diagnosis made by the model.</div></div><div><h3>Conclusion</h3><div>With sufficient high-quality data, machine learning models can provide high-sensitivity GC–MS interpretation and greatly improve the efficiency and quality of GC–MS based IEM screening.</div></div>\",\"PeriodicalId\":54950,\"journal\":{\"name\":\"International Journal of Medical Informatics\",\"volume\":\"195 \",\"pages\":\"Article 105765\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Medical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1386505624004283\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1386505624004283","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Training machine learning models to detect rare inborn errors of metabolism (IEMs) based on GC–MS urinary metabolomics for diseases screening
Background
Gas chromatography-mass spectrometry (GC–MS) has been shown to be a potentially efficient metabolic profiling platform in urine analysis. However, the widespread use of GC–MS for inborn errors of metabolism (IEM) screening is constrained by the rarity of IEM in population, and the difficult and specialized complexity of the interpretation of GC–MS organic acid profiles.
Methods
Based on 355,197 GC–MS test cases accumulated from 2013 to 2021 in China, a random forest-based machine learning model was proposed, trained, and evaluated. Weighted undersampling or oversampling data processing and staged modeling strategies were used to handle the highly imbalanced data and improve the ability of the model to identify different types of rare IEM cases.
Result
In the first-stage model, which only identified positive cases without discriminating the specific IEM, the screening sensitivity was 0.938 (or 0.991 if abnormal cases were also included). The average sensitivity of the second-stage models that classify 11 particular IEMs is 0.992, with an average specificity and accuracy of 0.944 and 0.969, respectively. The SHAP values visualized for each model explain the basis for the differential diagnosis made by the model.
Conclusion
With sufficient high-quality data, machine learning models can provide high-sensitivity GC–MS interpretation and greatly improve the efficiency and quality of GC–MS based IEM screening.
期刊介绍:
International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings.
The scope of journal covers:
Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.;
Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc.
Educational computer based programs pertaining to medical informatics or medicine in general;
Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.