糖尿病学家的生成人工智能:数据集分析简明教程。

IF 1.8 Q4 ENDOCRINOLOGY & METABOLISM Journal of Diabetes and Metabolic Disorders Pub Date : 2024-03-08 eCollection Date: 2024-06-01 DOI:10.1007/s40200-023-01377-0
Yoshiyasu Takefuji
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引用次数: 0

摘要

目的:本文旨在为糖尿病学家和内分泌学家提供使用生成式人工智能分析数据集的教程。本文旨在让那些初次接触生成式人工智能或没有编程经验的人也能理解:本文介绍了三个使用真实糖尿病数据集的示例。这些示例演示了使用 "组 "变量进行二元分类、交叉验证分析和 NT-proBNP 回归:结果:二元分类的预测准确率接近 0.9。然而,NT-proBNP 回归在该数据集上并不成功。计算得出的 R 平方值表明,预测模型与原始数据的拟合度较差:结论:NT-proBNP 回归不成功的原因可能是训练数据不足或需要额外的决定因素。数据集可能太小,或者需要新的指标来准确预测 NT-proBNP 回归值。用户必须对生成的代码进行验证,以确保它们能实现预期目标。
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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.

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来源期刊
Journal of Diabetes and Metabolic Disorders
Journal of Diabetes and Metabolic Disorders Medicine-Internal Medicine
CiteScore
4.80
自引率
3.60%
发文量
210
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.
期刊最新文献
The glucose tolerance peak parameter revisited. Definition for a novel use in Gestational Diabetes Mellitus confirmation. A severe pediatric life-threatening metabolic ketoacidosis. Generative AI for diabetologists: a concise tutorial on dataset analysis. Plasma acylcarnitines and amino acids in dyslipidemia: An integrated metabolomics and machine learning approach. Dietary anethole: a systematic review of its protective effects against metabolic syndrome.
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