在临终关怀中对死亡率进行准确预测至关重要但具有挑战性。现有的预后工具在各个时间框架中预测存活方面表现出中等水平，主要针对住院环境和单次评估。然而，这些工具可能无法捕捉到个体化和多样化的病人轨迹。对于在临终癌症患者中使用人工智能（AI）和可穿戴设备的证据有限。本研究旨在调查在临终癌症患者中使用可穿戴设备和AI预测死亡事件的潜力。我们的假设是通过智能手表的持续监测可以为临终患者的进展提供有价值的见解，并能够预测其病情的变化，从而最终增强个体化护理，尤其是在门诊或家庭护理设置中。该前瞻性研究在台湾大学医院进行。邀请接受癌症诊断和临终关怀的患者参与病房、门诊和家庭护理设置。每位参与者都会收到一只智能手表，用于收集生理数据，包括步数、心率、睡眠时间和血氧饱和度。每周进行临床评估。参与者将被随访至临终或最多52周。基于这些输入特征，我们评估了若干机器学习分类器和深度神经网络在7天死亡事件预测方面的性能。我们使用接受者操作特征曲线下的面积（AUROC）、F1得分、准确度和特异度作为评估指标。进一步进行Shapley加法解释值分析，以探索具有良好性能的模型。从2021年9月至2022年8月，总共收集了40名患者的1657个数据点，中位存活时间为34天，检测到28个死亡事件。在提出的模型中，极限梯度提升（XGBoost）获得了最佳结果，在测试集上的AUROC为96％，F1得分为78.5％，准确率为93％，特异度为97％。Shapley加法解释值分析确定了平均心率作为最重要的特征。其他重要特征包括步数、食欲、排尿状态和临床护理阶段。我们通过结合可穿戴设备和AI成功预测了未来7天患者死亡的情况。我们的研究结果突显了将AI和可穿戴技术整合到临床临终关怀中的潜力，为个性化患者护理提供了有价值的见解并支持临床决策。需要注意的是，我们的研究是在相对较小的样本中进行的，因此需要进一步研究来验证我们的方法，并评估其对临床护理的影响。ClinicalTrials.gov识别号NCT05054907；https://classic.clinicaltrials.gov/ct2/show/NCT05054907。©Jen-Hsuan Liu, Chih-Yuan Shih, Hsien-Liang Huang, Jen-Kuei Peng, Shao-Yi Cheng, Jaw-Shiun Tsai, Feipei Lai.《医学互联网研究》（https://www.jmir.org）, 2023年8月18日发表。

An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life.This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings.This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance.From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase.We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care.ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.©Jen-Hsuan Liu, Chih-Yuan Shih, Hsien-Liang Huang, Jen-Kuei Peng, Shao-Yi Cheng, Jaw-Shiun Tsai, Feipei Lai. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.08.2023.