{"title":"糖尿病学家的生成人工智能：数据集分析简明教程。","authors":"Yoshiyasu Takefuji","doi":"10.1007/s40200-023-01377-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>This paper aims to provide a tutorial for diabetologists and endocrinologists on using generative AI to analyze datasets. It is designed to be accessible to those new to generative AI or without programming experience.</p><p><strong>Methods: </strong>The paper presents three examples using a real diabetes dataset. The examples demonstrate binary classification with the 'Group' variable, cross-validation analysis, and NT-proBNP regression.</p><p><strong>Results: </strong>The binary classification achieved a prediction accuracy of nearly 0.9. However, the NT-proBNP regression was not successful with this dataset. The calculated R-squared values indicate a poor fit between the predicted model and the raw data.</p><p><strong>Conclusions: </strong>The unsuccessful NT-proBNP regression may be due to insufficient training data or the need for additional determinants. The dataset may be too small or new metrics may be required to accurately predict NT-proBNP regression values. It is crucial for users to verify the generated codes to ensure that they can achieve their desired objectives.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11196448/pdf/","citationCount":"0","resultStr":"{\"title\":\"Generative AI for diabetologists: a concise tutorial on dataset analysis.\",\"authors\":\"Yoshiyasu Takefuji\",\"doi\":\"10.1007/s40200-023-01377-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>This paper aims to provide a tutorial for diabetologists and endocrinologists on using generative AI to analyze datasets. It is designed to be accessible to those new to generative AI or without programming experience.</p><p><strong>Methods: </strong>The paper presents three examples using a real diabetes dataset. The examples demonstrate binary classification with the 'Group' variable, cross-validation analysis, and NT-proBNP regression.</p><p><strong>Results: </strong>The binary classification achieved a prediction accuracy of nearly 0.9. However, the NT-proBNP regression was not successful with this dataset. The calculated R-squared values indicate a poor fit between the predicted model and the raw data.</p><p><strong>Conclusions: </strong>The unsuccessful NT-proBNP regression may be due to insufficient training data or the need for additional determinants. The dataset may be too small or new metrics may be required to accurately predict NT-proBNP regression values. It is crucial for users to verify the generated codes to ensure that they can achieve their desired objectives.</p>\",\"PeriodicalId\":15635,\"journal\":{\"name\":\"Journal of Diabetes and Metabolic Disorders\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11196448/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Diabetes and Metabolic Disorders\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s40200-023-01377-0\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q4\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Diabetes and Metabolic Disorders","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40200-023-01377-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Generative AI for diabetologists: a concise tutorial on dataset analysis.
Objectives: This paper aims to provide a tutorial for diabetologists and endocrinologists on using generative AI to analyze datasets. It is designed to be accessible to those new to generative AI or without programming experience.
Methods: The paper presents three examples using a real diabetes dataset. The examples demonstrate binary classification with the 'Group' variable, cross-validation analysis, and NT-proBNP regression.
Results: The binary classification achieved a prediction accuracy of nearly 0.9. However, the NT-proBNP regression was not successful with this dataset. The calculated R-squared values indicate a poor fit between the predicted model and the raw data.
Conclusions: The unsuccessful NT-proBNP regression may be due to insufficient training data or the need for additional determinants. The dataset may be too small or new metrics may be required to accurately predict NT-proBNP regression values. It is crucial for users to verify the generated codes to ensure that they can achieve their desired objectives.
期刊介绍：
Journal of Diabetes & Metabolic Disorders is a peer reviewed journal which publishes original clinical and translational articles and reviews in the field of endocrinology and provides a forum of debate of the highest quality on these issues. Topics of interest include, but are not limited to, diabetes, lipid disorders, metabolic disorders, osteoporosis, interdisciplinary practices in endocrinology, cardiovascular and metabolic risk, aging research, obesity, traditional medicine, pychosomatic research, behavioral medicine, ethics and evidence-based practices.As of Jan 2018 the journal is published by Springer as a hybrid journal with no article processing charges. All articles published before 2018 are available free of charge on springerlink.Unofficial 2017 2-year Impact Factor: 1.816.
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