Papers with medical natural language processing
A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization (2020.emnlp-main)
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| Challenge: | Medical entity normalization (NEN) is a task that links medical mentions to entities in knowledge bases. |
| Approach: | They propose a sequence generative framework to generate Chinese medical procedure entity normalization by constraint decoding and category-based model refining. |
| Outcome: | The proposed model improves on baselines especially in the case of multi-implication Chinese medical procedures. |
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients (2020.lrec-1)
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Edward T. Moseley, Joy T. Wu, Jonathan Welt, John Foote, Patrick D. Tyler, David W. Grant, Eric T. Carlson, Sebastian Gehrmann, Franck Dernoncourt, Leo Anthony Celi
| Challenge: | Currently, most medical data is generated and stored in unstructured, text-based format. |
| Approach: | They propose to use a patient phenotyping dataset to identify whether a given medical condition is present in their notes. |
| Outcome: | The proposed dataset contains 1102 Discharge Summaries and 1000 Nursing Progress Notes. |
Medical Sentiment Analysis using Social Media: Towards building a Patient Assisted System (L18-1)
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| Challenge: | a study conducted by the pew Internet & American Life Project 1 shows that almost 80 percent of Internet users have explored health-related topic online. |
| Approach: | They propose to crawl medical forums with opinions about medical condition self narrated by users. |
| Outcome: | The proposed system is based on opinions about medical condition self-narrated by users on medical forums. |
Generating Multiple-choice Questions for Medical Question Answering with Distractors and Cue-masking (2024.lrec-main)
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| Challenge: | Medical multiple-choice question answering (MCQA) requires high accuracy to be useful in practice. |
| Approach: | They propose to focus masked language modeling on disease name prediction when using medical encyclopedic paragraphs as input. |
| Outcome: | The proposed model outperforms the masked language model on disease name prediction and masks the cues to the answers. |