机器学习和增强智能可在临床表现之前对 2 型糖尿病进行预测。

IF 2.4 Q3 ENDOCRINOLOGY & METABOLISM Current diabetes reviews Pub Date : 2024-02-01 DOI:10.2174/0115733998276990240117113408
Jonathan Rt Lakey, Krista Casazza, Waldemar Lernhardt, Eric J Mathur, Ian Jenkins
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引用次数: 0

摘要

背景:全球 2 型糖尿病(T2D)的发病率一直呈流行趋势。早期诊断和/或预防工作对于减轻多系统临床表现和由此造成的医疗负担至关重要。尽管对病理生理学的认识取得了巨大进步,治疗方法也在不断开发中,但有效性和可及性仍然是长期存在的限制因素。其中最大的挑战是,大量的研究工作尚未颁布用于早期检测和风险评估的可靠预测性生物标志物。新兴的多组学领域与机器学习(ML)和增强智能(AI)相结合,对预测、预防和个性化医疗的能力产生了深远影响:本文探讨了目前在确定 T2D 预测性生物标志物方面所面临的挑战,并讨论了生物标志物确定和验证的潜在可行解决方案:收录的文章来自 PubMed 查询。所选研究主题代表了糖尿病生物标志物预测和预后的广泛主题:目前诊断 T2D 的标准和临界值不是最佳的,也没有考虑到早期发现的各种因素。现在有机会利用人工智能和 ML 来大大提高对疾病内在机制的认识,并确定预后生物标志物。通过算法训练和验证,GATC 正在开发的创新技术有望在这一过程中发挥关键作用,实现对复杂生物系统的全面深入分析:结论:GATC 是一个新兴的领导者,它正在引导建立一种研究和预测个性化医学的系统方法。这些技术与临床数据的整合有助于更全面地了解 T2D,为精准医疗方法和改善患者预后铺平道路。
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Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation.

Background: The global incidence of type 2 diabetes (T2D) persists at epidemic proportions. Early diagnosis and/or preventive efforts are critical to attenuate the multi-systemic clinical manifestation and consequent healthcare burden. Despite enormous strides in the understanding of pathophysiology and on-going therapeutic development, effectiveness and access are persistent limitations. Among the greatest challenges, the extensive research efforts have not promulgated reliable predictive biomarkers for early detection and risk assessment. The emerging fields of multi-omics combined with machine learning (ML) and augmented intelligence (AI) have profoundly impacted the capacity for predictive, preventive, and personalized medicine.

Objective: This paper explores the current challenges associated with the identification of predictive biomarkers for T2D and discusses potential actionable solutions for biomarker identification and validation.

Methods: The articles included were collected from PubMed queries. The selected topics of inquiry represented a wide range of themes in diabetes biomarker prediction and prognosis.

Results: The current criteria and cutoffs for T2D diagnosis are not optimal nor consider a myriad of contributing factors in terms of early detection. There is an opportunity to leverage AI and ML to significantly enhance the understanding of the underlying mechanisms of the disease and identify prognostic biomarkers. The innovative technologies being developed by GATC are expected to play a crucial role in this pursuit via algorithm training and validation, enabling comprehensive and in-depth analysis of complex biological systems.

Conclusion: GATC is an emerging leader guiding the establishment of a systems approach towards research and predictive, personalized medicine. The integration of these technologies with clinical data can contribute to a more comprehensive understanding of T2D, paving the way for precision medicine approaches and improved patient outcomes.

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来源期刊
Current diabetes reviews
Current diabetes reviews ENDOCRINOLOGY & METABOLISM-
CiteScore
6.30
自引率
0.00%
发文量
158
期刊介绍: Current Diabetes Reviews publishes frontier reviews on all the latest advances on diabetes and its related areas e.g. pharmacology, pathogenesis, complications, epidemiology, clinical care, and therapy. The journal"s aim is to publish the highest quality review articles dedicated to clinical research in the field. The journal is essential reading for all researchers and clinicians who are involved in the field of diabetes.
期刊最新文献
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