Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature Feedback
Source abstract
Abstract
In medical texts, temporal information describes events and changes in status, such as medical visits and discharges. According to the semantic features, it is classified into simple time and complex time. The current research on time recognition usually focuses on coarse-grained simple time recognition while ignoring fine-grained complex time. To address this problem, based on the semantic concept of complex time in Clinical Time Ontology, we define seven basic features and eleven extraction rules and propose a complex medical time-extraction method. It combines probabilistic soft logic and textual feature feedback. The framework consists of two parts: (a) text feature recognition based on probabilistic soft logic, which is based on probabilistic soft logic for negative feedback adjustment; (b) complex medical time entity recognition based on text feature feedback, which is based on the text feature recognition model in (a) for positive feedback adjustment. Finally, the effectiveness of our approach is verified in text feature recognition and complex temporal entity recognition experimentally. In the text feature recognition task, our method shows the best F1 improvement of 18.09% on the Irregular Instant Collection type corresponding to utterance l17. In the complex medical temporal entity recognition task, the F1 metric improves the most significantly, by 10.42%, on the Irregular Instant Collection type.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Advanced Text Analysis Techniques · Biomedical Text Mining and Ontologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China
Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content, National Press and Publication Administration, Beijing 100038, China
Institute of Big Data Science and Engineering, Wuhan University of Science and Technology, Wuhan 430065, China
Funding
National Natural Science Foundation of China · Key Laboratory of Rich Media Digital Publishing, Content Organization and Knowledge Service · National key research and development program
References (44)
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