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[人工智能:重症医学中的挑战与应用]

[Artificial Intelligence: Challenges and Applications in Intensive Care Medicine].

作者信息

Martin Lukas, Peine Arne, Gronholz Maike, Marx Gernot, Bickenbach Johannes

出版信息

Anasthesiol Intensivmed Notfallmed Schmerzther. 2022 Mar;57(3):199-209. doi: 10.1055/a-1423-8006. Epub 2022 Mar 23.

Abstract

The high workload in intensive care medicine arises from the exponential growth of medical knowledge, the flood of data generated by the permanent and intensive monitoring of intensive care patients, and the documentation burden. Artificial intelligence (AI) is predicted to have a great impact on ICU work in the near future as it will be applicable in many areas of critical care medicine. These applications include documentation through speech recognition, predictions for decision support, algorithms for parameter optimisation and the development of personalised intensive care medicine. AI-based decision support systems can augment human therapy decisions. Primarily through machine learning, a sub-discipline of AI, self-adaptive algorithms can learn to recognise patterns and make predictions. For actual use in clinical settings, the explainability of such systems is a prerequisite. Intensive care staff spends a large amount of their working hours on documentation, which has increased up to 50% of work time with the introduction of PDMS. Speech recognition has the potential to reduce this documentation burden. It is not yet precise enough to be usable in the clinic. The application of AI in medicine, with the help of large data sets, promises to identify diagnoses more quickly, develop individualised, precise treatments, support therapeutic decisions, use resources with maximum effectiveness and thus optimise the patient experience in the near future.

摘要

重症医学的高工作量源于医学知识的指数级增长、重症监护患者持续密集监测所产生的数据洪流以及文档记录负担。预计人工智能(AI)在不久的将来将对重症监护室工作产生重大影响,因为它将适用于危重病医学的许多领域。这些应用包括通过语音识别进行文档记录、用于决策支持的预测、参数优化算法以及个性化重症医学的发展。基于人工智能的决策支持系统可以增强人类的治疗决策。主要通过人工智能的一个子学科——机器学习,自适应算法可以学会识别模式并进行预测。对于在临床环境中的实际应用,此类系统的可解释性是一个先决条件。重症监护室工作人员将大量工作时间花在文档记录上,随着电子病历管理系统(PDMS)的引入,这一比例已增至工作时间的50%。语音识别有可能减轻这种文档记录负担。但它目前还不够精确,无法在临床上使用。借助大数据集,人工智能在医学中的应用有望在不久的将来更快地识别诊断、制定个性化的精准治疗方案、支持治疗决策、以最大效率利用资源,从而优化患者体验。

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