背景:早期发现不良事件及其管理对于改善抗癌治疗结果至关重要,倾听患者的主观意见(患者的声音)可以为改善安全管理做出重大贡献。深度学习技术的最新进展为基于患者生成的文本数据评估安全相关事件提供了各种新方法。但很少有研究关注改善患者个体的实时安全监测。此外,尚未进行任何研究来验证深度学习模型,以筛选需要医学干预的临床重要不良事件信号的患者叙述.在我们之前的工作中,已经开发了新的深度学习模型来检测手足综合征的不良事件信号或限制患者日常生活的不良事件,旨在最终将它们用作个体患者的安全监控支持工具。
目的:本研究旨在评估我们的深度学习模型是否可以筛选需要医疗保健专业人员干预的临床重要不良事件信号。还评估了我们的深度学习模型对药房患者担忧数据的适用性。
方法:社区药房的药学服务记录被用于评估我们的深度学习模型。记录遵循SOAP格式,由主观(S)组成,目标(O),评估(A),和计划(P)栏。由于患者在S栏的关注和药剂师的专业记录的独特组合,这被认为是适合本目的的数据.我们的深度学习模型被应用于癌症患者的S记录,并对提取的不良事件信号与医疗行为和处方药进行评估.
结果:从2479名患者的30,784S记录中,至少有1种抗癌药物处方,我们的深度学习模型对手足综合征(n=152,91%)和限制患者日常生活的不良事件(n=157,80.1%)均有超过80%的准确率.深度学习模型还能够筛选需要医疗保健提供者进行医疗干预的不良事件信号。根据对处方抗癌药物的分析,提取的不良事件信号可以反映患者使用的抗癌药物的副作用。“疼痛或麻木”(n=57,36.3%),“发烧”(n=46,29.3%),和“恶心”(n=40,25.5%)是由该模型确定的限制患者日常生活的不良事件的真实不良事件信号中的常见症状.
结论:我们的深度学习模型能够筛选需要对症状进行干预的临床重要不良事件信号。研究还证实,这些深度学习模型可以应用于药剂师日常工作中积累的药物护理记录中记录的患者主观信息。
BACKGROUND: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients\' subjective opinions (patients\' voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients\' narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients\' daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients.
OBJECTIVE: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients\' concerns at pharmacies was also assessed.
METHODS: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients\' concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs.
RESULTS: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients\' daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. \"Pain or numbness\" (n=57, 36.3%), \"fever\" (n=46, 29.3%), and \"nausea\" (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients\' daily lives.
CONCLUSIONS: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients\' subjective information recorded in pharmaceutical care records accumulated during pharmacists\' daily work.