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脑海中的数据集:ALS和MS进展建模的临床,可穿戴和环境数据.

Guglielmo Faggioli1, Laura Menotti2, Stefano Marchesin3

  • 1Department of Information Engineering, University of Padova, Padova, Italy. guglielmo.faggioli@unipd.it.

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概括

对肌缩侧面硬化症 (ALS) 和多发性硬化症 (MS) 的新数据集推进了AI疾病进展建模. 这些现实世界的临床数据集支持开发工具,以改善患者护理和治疗策略.

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科学领域:

  • 神经科学是一个神经科学.
  • 医疗信息学 医疗信息学
  • 人工智能的人工智能

背景情况:

  • 肌缩侧面硬化症 (ALS) 和多发性硬化症 (MS) 是一种进展性神经系统疾病.
  • 对ALS和MS进展的预测建模对于患者护理至关重要,但由于数据的可用性而受到限制.
  • 人工智能 (AI) 具有改善疾病进展建模的潜力.

研究的目的:

  • 策划和验证用于ALS和MS的AI驱动疾病进展建模的全面数据集.
  • 为了应对数据稀缺的挑战,阻碍了预测工具的发展.
  • 支持创建人工智能模型,用于个性化患者护理和临床决策.

主要方法:

  • 策划了H2020 BRAINTEASER项目的四个数据集,包括临床,环境和可穿戴数据.
  • 收集了来自现实世界临床实践的2,290名ALS患者和723名MS患者的数据.
  • 通过CLEF智能疾病进展预测挑战的三版验证数据集,以及自动和手动质量检查.

主要成果:

  • 建立了针对ALS和MS患者进展的大型,临床相关数据集.
  • 数据集包括各种数据类型,为人工智能模型培训提供现实的表示.
  • 通过挑战进行社区验证可以确保数据集的质量和用于预测建模的实用性.

结论:

  • BRAINTEASER数据集为推进神经疾病研究中的AI提供了宝贵的资源.
  • 这些数据集有助于开发和验证用于预测ALS和MS进展的AI工具.
  • 改进的预测工具可以提高患者的治疗结果,并支持这些衰弱性疾病的临床管理.