Division of biostatistics, School of Public Health, university of California Berkeley, CA, USA; Department of anesthesia and perioperative medicine, university of California San Francisco, CA, USA; Service d'anesthésie-réanimation, hôpital Européen Georges-Pompidou, université Paris Descartes, Sorbonne Paris Cite, 75015 Paris, France; Service de biostatistique et informatique médicale, hôpital Saint-Louis, Inserm UMR-1153, université Paris Diderot, Sorbonne Paris Cite, 75010 Paris, France.
Department of surgery, university of Colorado Denver, Colorado, USA.
Anaesth Crit Care Pain Med. 2019 Aug;38(4):377-384. doi: 10.1016/j.accpm.2018.09.008. Epub 2018 Oct 16.
Historically, personalised medicine has been synonymous with pharmacogenomics and oncology. We argue for a new framework for personalised medicine analytics that capitalises on more detailed patient-level data and leverages recent advances in causal inference and machine learning tailored towards decision support applicable to critically ill patients. We discuss how advances in data technology and statistics are providing new opportunities for asking more targeted questions regarding patient treatment, and how this can be applied in the intensive care unit to better predict patient-centred outcomes, help in the discovery of new treatment regimens associated with improved outcomes, and ultimately how these rules can be learned in real-time for the patient.
从历史上看，个性化医学一直是药物基因组学和肿瘤学的代名词。我们主张建立一个新的个性化医学分析框架，该框架利用更详细的患者层面数据，并利用最近在因果推理和机器学习方面的进展，为适用于重症患者的决策支持提供支持。我们讨论了数据技术和统计学的进步如何为提出更有针对性的问题提供了新的机会，以及如何将其应用于重症监护病房，以更好地预测以患者为中心的结果，帮助发现与改善结果相关的新治疗方案，以及最终如何实时为患者学习这些规则。
