Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006, China.
Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006, China.
Biomed Eng Online. 2023 Sep 1;22(1):87. doi: 10.1186/s12938-023-01148-1.
Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.
现代组学技术可以产生大量的生物医学数据，为个体化精准医学提供了前所未有的机会。然而，传统的统计学方法无法有效地处理和利用这些大数据。为了应对这一新的挑战，机器学习算法近年来得到了迅速的发展和应用，能够降低维度、提取特征、组织数据并形成可自动操作的数据驱动临床决策系统。数据驱动的临床决策在精准医学中有广阔的应用前景，已在消化系统疾病中进行了研究，包括早期诊断和筛查、消化系统恶性肿瘤的分子分型、分期和分层，以及克罗恩病的精确诊断、影像学和内镜检查的辅助诊断、囊性病变的鉴别诊断、急性腹痛的病因鉴别、上消化道出血（UGIB）分层和食管动力功能的实时诊断，显示出良好的应用前景。本文综述了数据驱动的临床决策在消化系统疾病精准诊断中的最新进展，并在简要介绍数据驱动决策方法之后讨论了数据驱动决策的局限性。
