| Challenge: | Medical professionals search the literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s). |
| Approach: | They propose to exploit the availability of structured abstracts to extract medically relevant information from syntactic patterns. |
| Outcome: | The proposed models differ from the constituent unigrams in the extracted patterns, suggesting that they capture contextual information that is otherwise lost. |
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| Challenge: | Existing methods for medical relation extraction use dependency syntax as a source of features. |
| Approach: | They propose a method to extract relational information from medical literature by using dependency forests. |
| Outcome: | The proposed method outperforms the standard tree-based methods in the medical domain. |
Incorporating medical knowledge in BERT for clinical relation extraction (2021.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) are used for diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question/Answering. |
| Approach: | They propose to add medical knowledge to pre-trained language models to facilitate clinical relation extraction using a large text corpus. |
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Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
| Approach: | They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form . |
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Large language models are few-shot clinical information extractors (2022.emnlp-main)
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| Challenge: | a long-running goal of clinical NLP is the extraction of important variables trapped in clinical notes. |
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The Medical Scribe: Corpus Development and Model Performance Analyses (2020.lrec-1)
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Izhak Shafran, Nan Du, Linh Tran, Amanda Perry, Lauren Keyes, Mark Knichel, Ashley Domin, Lei Huang, Yu-hui Chen, Gang Li, Mingqiu Wang, Laurent El Shafey, Hagen Soltau, Justin Stuart Paul
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A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature (P18-1)
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| Challenge: | In 2015 alone, about 100 manuscripts describing randomized controlled trials for medical interventions were published every day. |
| Approach: | They propose a corpus of 5,000 medical articles annotated with demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured. |
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Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web (2020.acl-tutorials)
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| Challenge: | a tutorial explores the commonalities in the challenges and solutions developed to address information extraction from the World Wide Web. |
| Approach: | This tutorial examines methods for extracting information from the World Wide Web . it explores the commonalities in the challenges and solutions developed to address these different forms of text . |
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Few-shot fine-tuning SOTA summarization models for medical dialogues (2022.naacl-srw)
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| Challenge: | Abstractive summarization of medical dialogues is a challenge for standard training approaches due to the paucity of suitable datasets. |
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Generative Models for Automatic Medical Decision Rule Extraction from Text (2024.emnlp-main)
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| Challenge: | Medical decision rules are traditionally constructed by medical experts, which is expensive and hard to scale up. |
| Approach: | They propose to extract medical decision rules from text using generative models . their code will be open-source upon acceptance . |
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Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction (2024.findings-acl)
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| Challenge: | Document collections of various domains share some underlying collection-wide structure . structure can be useful in various use cases across different domains, such as legal, medical, or financial . |
| Approach: | They propose to identify the typical structure of document within a collection by using header paraphrases to ground topics to respective document locations. |
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