本文主要是介绍综述 2022-Nature Medicine ：AI+生物医学，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
Acosta, Julián N., et al. "Multimodal biomedical AI." Nature Medicine 28.9 (2022): 1773-1784.
被引次数：210
一、生物医学数据类型
biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing
翻译：大型生物库、电子健康记录(EHR)、医学成像（CT或者视网膜成像）、可穿戴和环境生物传感器的生物医学数据（包括心率、睡眠、体力活动、心电图、血氧饱和度和血糖监测）、组学数据(基因组、蛋白质组、转录组、免疫组、表观基因组、代谢组和微生物组)
二、可解决的问题
personalized medicine, digital clinical trials, remote monitoring and care, pandemic surveillance, digital twin technology and virtual health assistants
翻译：个性化医疗、数字临床试验、远程监控和护理、流行病监测、数字孪生技术和虚拟健康助理
三、和EHR相关的待读论文
EHR平台
种族/族裔、血统、收入水平、教育水平、医疗保健、年龄、残疾状况、地理位置、性别角度研究
Jabbour, S., Fouhey, D., Kazerooni, E., Wiens, J. & Sjoding, M. W. Combining chest X-rays and electronic health record data using machine learning to diagnose acute respiratory failure. J. Am. Med. Inform. Assoc. 29, 1060–1068 (2022).
Golbus, J. R., Pescatore, N. A., Nallamothu, B. K., Shah, N. & Kheterpal, S. Wearable device signals and home blood pressure data across age, sex, race, ethnicity, and clinical phenotypes in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) study: a prospective, community-based observational study. Lancet Digit. Health 3, e707–e715 (2021).
Haneuse, S., Arterburn, D. & Daniels, M. J. Assessing missing data assumptions in EHR-based studies: a complex and underappreciated task. JAMA Netw. Open 4, e210184–e210184 (2021).
Li, J. et al. Imputation of missing values for electronic health record laboratory data. NPJ Digit. Med. 4, 147 (2021).
Tang, S. et al. Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. J. Am. Med. Inform. Assoc. 27, 1921–1934 (2020).
这篇关于综述 2022-Nature Medicine ：AI+生物医学的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
