Jonathan Rt Lakey, Krista Casazza, Waldemar Lernhardt, Eric J Mathur, Ian Jenkins
{"title":"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。","authors":"Jonathan Rt Lakey, Krista Casazza, Waldemar Lernhardt, Eric J Mathur, Ian Jenkins","doi":"10.2174/0115733998276990240117113408","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":10825,"journal":{"name":"Current diabetes reviews","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation.\",\"authors\":\"Jonathan Rt Lakey, Krista Casazza, Waldemar Lernhardt, Eric J Mathur, Ian Jenkins\",\"doi\":\"10.2174/0115733998276990240117113408\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":10825,\"journal\":{\"name\":\"Current diabetes reviews\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current diabetes reviews\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/0115733998276990240117113408\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current diabetes reviews","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0115733998276990240117113408","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
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.
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.