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用于调查同意 COVID-19 患者和精准医学的智能健康系统。

Intelligent health system for the investigation of consenting COVID-19 patients and precision medicine.

机构信息

Rutgers Institute for Health, Health Care Policy & Aging Research, Rutgers University, 112 Paterson Street, New Brunswick, NJ 08901, USA.

Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical & Health Sciences, 125 Paterson Street, New Brunswick, NJ 08901, USA.

出版信息

Per Med. 2021 Sep;18(6):573-582. doi: 10.2217/pme-2021-0068. Epub 2021 Oct 8.

DOI:10.2217/pme-2021-0068
PMID:34619976
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8544483/
Abstract

Advancing frontiers of clinical research, we discuss the need for intelligent health systems to support a deeper investigation of COVID-19. We hypothesize that the convergence of the healthcare data and staggering developments in artificial intelligence have the potential to elevate the recovery process with diagnostic and predictive analysis to identify major causes of mortality, modifiable risk factors and actionable information that supports the early detection and prevention of COVID-19. However, current constraints include the recruitment of COVID-19 patients for research; translational integration of electronic health records and diversified public datasets; and the development of artificial intelligence systems for data-intensive computational modeling to assist clinical decision making. We propose a novel nexus of machine learning algorithms to examine COVID-19 data granularity from population studies to subgroups stratification and ensure best modeling strategies within the data continuum.

摘要

推进临床研究前沿，我们探讨了智能健康系统支持对 COVID-19 进行更深入研究的必要性。我们假设，医疗保健数据的融合以及人工智能的惊人发展有可能通过诊断和预测分析来提升康复过程，以识别主要死亡原因、可改变的风险因素和支持 COVID-19 的早期检测和预防的可操作信息。然而，目前的限制包括为研究招募 COVID-19 患者；电子健康记录和多样化的公共数据集的转化整合；以及开发用于数据密集型计算建模的人工智能系统，以协助临床决策。我们提出了一种新的机器学习算法网络，以从人群研究到亚组分层检查 COVID-19 数据的粒度，并确保在数据连续体中采用最佳建模策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e48/8544483/3cdb47a56a81/figure5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e48/8544483/d18c440cc7dd/figure1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e48/8544483/80df8a88b207/figure2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e48/8544483/0dedde49caed/figure3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e48/8544483/45368b802df5/figure4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e48/8544483/3cdb47a56a81/figure5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e48/8544483/d18c440cc7dd/figure1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e48/8544483/80df8a88b207/figure2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e48/8544483/0dedde49caed/figure3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e48/8544483/45368b802df5/figure4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e48/8544483/3cdb47a56a81/figure5.jpg

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