Understanding the need for digital twins' data in patient advocacy and forecasting oncology.
Understanding the need for digital twins' data in patient advocacy and forecasting oncology.
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DOI：
10.3389/frai.2023.1260361
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发表时间：
2023
影响因子：
4
通讯作者：
Mccoy, Matthew D.
中科院分区：
文献类型：
--
作者：
Chang, Hung-Ching;Gitau, Antony M.;Kothapalli, Siri;Welch, Danny R.;Sardiu, Mihaela E.;Mccoy, Matthew D.
数字孪生由现实世界组件和虚拟组件构成，在现实世界组件中进行数据测量，在虚拟组件中这些测量值被用于设定计算模型的参数。人们越来越有兴趣应用基于数字孪生的方法来优化个性化治疗方案并改善健康结果。人工智能的集成在这一过程中至关重要，因为它使能够开发出复杂的疾病模型，从而准确预测患者对治疗干预的反应。当人工智能应用于医疗干预时，它在数字孪生的现实世界组件中有一个独特且同样重要的应用。患者只能接受一次治疗，因此，我们必须借助先前接受治疗的患者的经验和结果来验证和优化计算预测。数字孪生的物理组件必须利用先前接受治疗的癌症患者的现有数据汇编，这些患者的特征（基因、肿瘤类型、生活方式等）与新诊断的癌症患者非常相似，目的是预测结果、对治疗方案进行分层、预测对治疗的反应和/或不良事件。这些任务包括开发可靠的数据收集方法、确保数据的可用性、创建精确且可靠的模型以及为数据的使用和共享制定道德准则。要在临床护理中成功应用数字孪生技术，收集准确反映疾病种类和人群多样性的数据至关重要。
Digital twins are made of a real-world component where data is measured and a virtual component where those measurements are used to parameterize computational models. There is growing interest in applying digital twins-based approaches to optimize personalized treatment plans and improve health outcomes. The integration of artificial intelligence is critical in this process, as it enables the development of sophisticated disease models that can accurately predict patient response to therapeutic interventions. There is a unique and equally important application of AI to the real-world component of a digital twin when it is applied to medical interventions. The patient can only be treated once, and therefore, we must turn to the experience and outcomes of previously treated patients for validation and optimization of the computational predictions. The physical component of a digital twins instead must utilize a compilation of available data from previously treated cancer patients whose characteristics (genetics, tumor type, lifestyle, etc.) closely parallel those of a newly diagnosed cancer patient for the purpose of predicting outcomes, stratifying treatment options, predicting responses to treatment and/or adverse events. These tasks include the development of robust data collection methods, ensuring data availability, creating precise and dependable models, and establishing ethical guidelines for the use and sharing of data. To successfully implement digital twin technology in clinical care, it is crucial to gather data that accurately reflects the variety of diseases and the diversity of the population.
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影响因子：
30.8
作者：
Weinstein, John N.;Collisson, Eric A.;Mills, Gordon B.;Shaw, Kenna R. Mills;Ozenberger, Brad A.;Ellrott, Kyle;Shmulevich, Ilya;Sander, Chris;Stuart, Joshua M.
通讯作者：
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DOI：
10.1080/07391102.2020.1815578
发表时间：
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