目的：探讨高龄老人临终治疗方式和地点需求的选择，为临床更好的开展临终关怀护理服务，提高患者生存质量，建立完善的临终关怀服务体系提供科学依据。方法：结合相关量表自行设计问卷对204名80岁以上的住院患者进行调查。结果：在选择临终治疗方式时，依次为对症治疗、听取医生的安排、选择不治疗、希望继续高技术治疗、听取子女们的安排。选择临终地点时，依次为愿意在医院、在家中和在养老院渡过生命的最后时光。结论：高龄老人的临终需求是多元化的，因此，在临床工作中应针对不同的需求提供个性化护理。
Subject recruitment is a key component that affects the progress and results of clinical trials, and generally conducted with eligibility criteria (includes inclusion criteria and exclusion criteria). The semantic category analysis of eligibility criteria can help optimizing clinical trials design and building automated patient recruitment system. This study explored the automatic semantic categories classification of Chinese eligibility criteria based on artificial intelligence by academic shared task. We totally collected 38 341 annotated eligibility criteria sentences and predefined 44 semantic categories. A total of 75 teams participated in competition, with 27 teams having submitted system outputs. Based on the results, we found out that most teams adopted mixed models. The mainstream resolution was applying pre-trained language models capable of providing rich semantic representation, which were combined with neural network models and used to fine-tune the models with reference to classifier tasks, and finally improved classification performance could be obtained by ensemble modeling. The best-performing system achieved a macro F1 score of 0.81 by using a pre-trained language model, i.e. bidirectional encoder representations from transformers (BERT) and ensemble modeling. With the error analysis we found out that from the point of data processing steps the data pre-processing and post-processing were very important for classification, while from the point of data volume these categories with less data volume showed lower classification performance. Finally, we hope that this study could provide a valuable dataset and state-of-the-art result for the research of Chinese medical short text classification.