Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature Feedback
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 recogni...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/5/3348 |
| _version_ | 1827752905743007744 |
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| author | Jinguang Gu Daiwen Wang Danyang Hu Feng Gao Fangfang Xu |
| author_facet | Jinguang Gu Daiwen Wang Danyang Hu Feng Gao Fangfang Xu |
| author_sort | Jinguang Gu |
| collection | DOAJ |
| description | 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 <i>F1</i> improvement of 18.09% on the Irregular Instant Collection type corresponding to utterance <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>l</mi><mn>17</mn></msub></semantics></math></inline-formula>. In the complex medical temporal entity recognition task, the <i>F1</i> metric improves the most significantly, by 10.42%, on the Irregular Instant Collection type. |
| first_indexed | 2024-03-11T07:29:28Z |
| format | Article |
| id | doaj.art-1bdcab5bdb6b4ee78bf5888174bf834d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:29:28Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1bdcab5bdb6b4ee78bf5888174bf834d2023-11-17T07:22:42ZengMDPI AGApplied Sciences2076-34172023-03-01135334810.3390/app13053348Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature FeedbackJinguang Gu0Daiwen Wang1Danyang Hu2Feng Gao3Fangfang Xu4College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, ChinaCollege of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, ChinaCollege of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, ChinaCollege of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, ChinaCollege of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, ChinaIn 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 <i>F1</i> improvement of 18.09% on the Irregular Instant Collection type corresponding to utterance <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>l</mi><mn>17</mn></msub></semantics></math></inline-formula>. In the complex medical temporal entity recognition task, the <i>F1</i> metric improves the most significantly, by 10.42%, on the Irregular Instant Collection type.https://www.mdpi.com/2076-3417/13/5/3348probabilistic soft logictemporal entity extractionlogic rulestext feature automationtext feature feedback |
| spellingShingle | Jinguang Gu Daiwen Wang Danyang Hu Feng Gao Fangfang Xu Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature Feedback Applied Sciences probabilistic soft logic temporal entity extraction logic rules text feature automation text feature feedback |
| title | Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature Feedback |
| title_full | Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature Feedback |
| title_fullStr | Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature Feedback |
| title_full_unstemmed | Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature Feedback |
| title_short | Temporal Extraction of Complex Medicine by Combining Probabilistic Soft Logic and Textual Feature Feedback |
| title_sort | temporal extraction of complex medicine by combining probabilistic soft logic and textual feature feedback |
| topic | probabilistic soft logic temporal entity extraction logic rules text feature automation text feature feedback |
| url | https://www.mdpi.com/2076-3417/13/5/3348 |
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