The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae.
The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae.
复制标题
急性护理环境中机器学习的机遇和挑战，以预防创伤后压力后遗症。
DOI：
10.1016/j.expneurol.2020.113526
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发表时间：
2021-03
影响因子：
5.3
通讯作者：
Chang BP
中科院分区：
文献类型：
--
作者：
Schultebraucks K;Chang BP
个性化医疗是最近临床研究中最令人兴奋的创新之一，它提供了在个人层面进行量身定制的筛查和管理的机会。由于选择性地排除了不太可能受益的患者，生物标志物富集的临床试验已经显示出癌症研究的效率和信息量的提高。在急性应激情况下，临床重要的决定往往是在时间敏感的方式下做出的，提供者可能被迫根据简短的临床评估做出决定。高达30%的创伤幸存者进入急诊科（ED）将发展长期的创伤后应激精神病理。这些创伤后应激后遗症的幸存者的长期影响是显著的，影响长期的心理和生理恢复。谁会出现创伤后应激症状的准确预后模型尚不存在。此外，尽管在预防措施最有效的所谓黄金时间，特别是急症护理环境为预防提供了一个关键的机会窗口，但目前还没有一种可扩展的、具有成本效益的方法可以轻易地纳入常规护理。在这篇综述中，我们旨在讨论新兴的机器学习（ML）应用，这些应用有望在急性护理环境中进行精确的风险分层和靶向治疗。本综述的目的是介绍数字健康创新的例子，并讨论这些新方法在急性护理环境中治疗选择和预防创伤后后遗症方面的潜力。基于人工智能的解决方案的应用已经在其他领域取得了巨大的成功，并且正在迅速接近心理护理领域。基于算法的风险预测新方法和数字表型的使用为预测急性护理环境中创伤后应激障碍的未来风险提供了很高的潜力，并在精确精神病学方面迈出了新的步伐。
Personalized medicine is among the most exciting innovations in recent clinical research, offering the opportunity for tailored screening and management at the individual level. Biomarker-enriched clinical trials have shown increased efficiency and informativeness in cancer research due to the selective exclusion of patients unlikely to benefit. In acute stress situations, clinically significant decisions are often made in time-sensitive manners and providers may be pressed to make decisions based on abbreviated clinical assessments. Up to 30% of trauma survivors admitted to the Emergency Department (ED) will develop long-lasting posttraumatic stress psychopathologies. The long-term impact of those survivors with posttraumatic stress sequelae are significant, impacting both long-term psychological and physiological recovery. An accurate prognostic model of who will develop posttraumatic stress symptoms does not exist yet. Additionally, no scalable and cost-effective method that can be easily integrated into routine care exists, even though especially the acute care setting provides a critical window of opportunity for prevention in the so-called golden hours when preventive measures are most effective. In this review, we aim to discuss emerging machine learning (ML) applications that are promising for precisely risk stratification and targeted treatments in the acute care setting. The aim of this review is to present examples of digital health innovations and to discuss the potential of these new approaches for treatment selection and prevention of posttraumatic sequelae in the acute care setting. The application of artificial intelligence-based solutions have already had great success in other areas and are rapidly approaching the field of psychological care as well. New ways of algorithm-based risk predicting, and the use of digital phenotypes provide a high potential for predicting future risk of PTSD in acute care settings and to go new steps in precision psychiatry.
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影响因子：
5.4
作者：
Doherty, Sean T.;Lemieux, Christopher J.;Canally, Culum
通讯作者：
Canally, Culum
影响因子：
11
作者：
Dean KR;Hammamieh R;Mellon SH;Abu-Amara D;Flory JD;Guffanti G;Wang K;Daigle BJ Jr;Gautam A;Lee I;Yang R;Almli LM;Bersani FS;Chakraborty N;Donohue D;Kerley K;Kim TK;Laska E;Young Lee M;Lindqvist D;Lori A;Lu L;Misganaw B;Muhie S;Newman J;Price ND;Qin S;Reus VI;Siegel C;Somvanshi PR;Thakur GS;Zhou Y;PTSD Systems Biology Consortium;Hood L;Ressler KJ;Wolkowitz OM;Yehuda R;Jett M;Doyle FJ 3rd;Marmar C
通讯作者：
Marmar C
影响因子：
3.7
作者：
Edmondson D;Richardson S;Falzon L;Davidson KW;Mills MA;Neria Y
通讯作者：
Neria Y
DOI：
10.1007/bf02190342
发表时间：
1992-09-01
影响因子：
4.7
作者：
GAEBEL, W;WOLWER, W
通讯作者：
WOLWER, W
影响因子：
3.3
作者：
Carmi, Lior;Fostick, Leah;Zohar, Joseph
通讯作者：
Zohar, Joseph