Scholarlink

<p>This study introduces Glucose Level Understanding and Control Optimized for Safety and Efficacy (GLUCOSE), a distributional offline reinforcement learning algorithm for optimizing insulin dosing after cardiac surgery. Trained on 5228 patients, tested on 920, and externally validated on 649, GLUCOSE achieved a mean estimated reward of 0.0 [–0.07, 0.06] in internal testing and –0.63 [–0.74, –0.52] in external validation, outperforming clinician returns of –1.29 [–1.37, –1.20] and –1.02 [–1.16, –0.89]. In multi-phase human validation, GLUCOSE first showed a significantly lower mean absolute error (MAE) in insulin dosing, with 0.9 units MAE versus clinicians’ 1.97 units (<i>p</i> &lt; 0.001) in internal testing and 1.90 versus 2.24 units (<i>p</i> = 0.003) in external validation. The second and third phases found GLUCOSE’s performance as comparable to or exceeding that of senior clinicians in MAE, safety, effectiveness, and acceptability. These findings suggest GLUCOSE as a robust tool for improving postoperative glucose management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A distributional reinforcement learning model for optimal glucose control after cardiac surgery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月27日

查看全文
  • Jacob M. Desman,
  • Zhang-Wei Hong,
  • Moein Sabounchi,
  • Ashwin S. Sawant,
  • Jaskirat Gill,
  • Ana C. Costa,
  • Gagan Kumar,
  • Rajeev Sharma,
  • Arpeta Gupta,
  • Paul McCarthy,
  • Veena Nandwani,
  • Doug Powell,
  • Alexandra Carideo,
  • Donnie Goodwin,
  • Sanam Ahmed,
  • Umesh Gidwani,
  • Matthew A. Levin,
  • Robin Varghese,
  • Farzan Filsoufi,
  • Robert Freeman,
  • Avniel Shetreat-Klein,
  • Alexander W. Charney,
  • Ira Hofer,
  • Lili Chan,
  • David Reich,
  • Patricia Kovatch,
  • Roopa Kohli-Seth,
  • Monica Kraft,
  • Pulkit Agrawal,
  • John A. Kellum,
  • Girish N. Nadkarni,
  • Ankit Sakhuja

摘要

This study introduces Glucose Level Understanding and Control Optimized for Safety and Efficacy (GLUCOSE), a distributional offline reinforcement learning algorithm for optimizing insulin dosing after cardiac surgery. Trained on 5228 patients, tested on 920, and externally validated on 649, GLUCOSE achieved a mean estimated reward of 0.0 [–0.07, 0.06] in internal testing and –0.63 [–0.74, –0.52] in external validation, outperforming clinician returns of –1.29 [–1.37, –1.20] and –1.02 [–1.16, –0.89]. In multi-phase human validation, GLUCOSE first showed a significantly lower mean absolute error (MAE) in insulin dosing, with 0.9 units MAE versus clinicians’ 1.97 units (p < 0.001) in internal testing and 1.90 versus 2.24 units (p = 0.003) in external validation. The second and third phases found GLUCOSE’s performance as comparable to or exceeding that of senior clinicians in MAE, safety, effectiveness, and acceptability. These findings suggest GLUCOSE as a robust tool for improving postoperative glucose management.