文件名称：Improving Palliative Care with Deep Learning
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更新时间：2021-07-04 03:41:54
国外文献
基于深度学习的姑息治疗 国外文献 以下为摘要： Abstract Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prog- noses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life . We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model s predictions. 提高医院病人临终关怀的质量是医疗机构的首要任务。研究表明，医生倾向于高估预后，这与治疗惯性结合导致患者愿望与生命结束时的实际治疗之间不匹配。我们描述了一种使用深度学习和电子健康记录（EHR）数据来解决这个问题的方法，该数据目前正在由学术医疗中心通过机构审查委员会的批准。入院患者的EHR数据通过一种算法自动评估，该算法可以将可能从姑息治疗服务中受益的患者引入姑息治疗团队的关注。该算法是一个深度神经网络，根据前几年的EHR数据进行训练，预测患者的全因3-12个月死亡率，作为可从姑息治疗获益的患者的替代指标。我们的预测使姑息治疗团队能够采取积极主动的方式接触这些患者，而不是依靠治疗医师的推荐，或对所有患者进行耗时的图表审查。我们还提供了一种新颖的解释技术，我们用它来解释模型的预测。