大阪大学 · 医療専門職
Hiroe Seto教授の研究室では、ビッグデータを活用した機械学習を用いた疾患予測モデルの構築とその信頼性評価を主眼としています。特に、勾配ブースティング決定木（GBDT）とロジスティック回帰の比較を通じて、糖尿病予測におけるモデルのキャリブレーション性能を精査しています。また、従来の予測確率に基づく評価では不十分なため、変数ごとのキャリブレーションを視覚的・定量的に評価する新規手法の開発にも取り組んでいます。
Figures are computed from collected data and may differ slightly.
We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction
This finding, complex seasonal variations of MetS prevalence, WC, TG, and FPG, could not be derived from previous studies using just the mean values in spring, summer, autumn and winter or the cosinor analysis. More attention should be paid to factors affecting seasonal variations of central obesity, dyslipidemia and insulin resistance.
In developing risk prediction models for specific diseases, it is essential to evaluate the calibration performance of the prediction model. Various methods have been proposed to assess the calibration of prediction models, but it has been pointed out that conventional methods based on the predicted probability of the model are insufficient to detect miscalibration. Another problem is that a method for evaluating calibration for continuous variables of interest has not yet been established. We t
<title>Abstract</title> We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were
<title>Abstract</title> In developing risk prediction models for specific diseases, it is essential to evaluate the calibration performance of the prediction model. Various methods have been proposed to assess the calibra- tion of prediction models, but it has been pointed out that conventional methods based on the predicted probability of the model are insufficient to detect miscalibration. Another problem is the inability to assess calibration for variables of interest, such as covariate of hi
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