图 书 馆
基于转导学习的远程监控在电子病历中识别药物不良反应Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records |
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| 课程网址: | http://videolectures.net/kdd2017_taewijit_electronic_medical_reco... |
| 主讲教师: | Siriwon Taewijit |
| 开课单位: | 日本高等科学技术研究院 |
| 开课时间: | 2017-12-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively. |
| 关 键 词: | 不良反应; 临床文本; 监督学习 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-04-16：chenxin01 |
| 最后编审: | 2023-05-21：chenxin01 |
| 阅读次数: | 30 |