Jiarui Yao, S. Bethard, Kristin Wright-Bettner, Eli Goldner, D. Harris, G. Savova
{"title":"Textual Entailment for Temporal Dependency Graph Parsing","authors":"Jiarui Yao, S. Bethard, Kristin Wright-Bettner, Eli Goldner, D. Harris, G. Savova","doi":"10.18653/v1/2023.clinicalnlp-1.25","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.25","url":null,"abstract":"We explore temporal dependency graph (TDG) parsing in the clinical domain. We leverage existing annotations on the THYME dataset to semi-automatically construct a TDG corpus. Then we propose a new natural language inference (NLI) approach to TDG parsing, and evaluate it both on general domain TDGs from wikinews and the newly constructed clinical TDG corpus. We achieve competitive performance on general domain TDGs with a much simpler model than prior work. On the clinical TDGs, our method establishes the first result of TDG parsing on clinical data with 0.79/0.88 micro/macro F1.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131706889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geunyeong Jeong, Juoh Sun, Seokwon Jeong, Hyunjin Shin, Harksoo Kim
{"title":"Improving Automatic KCD Coding: Introducing the KoDAK and an Optimized Tokenization Method for Korean Clinical Documents","authors":"Geunyeong Jeong, Juoh Sun, Seokwon Jeong, Hyunjin Shin, Harksoo Kim","doi":"10.18653/v1/2023.clinicalnlp-1.12","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.12","url":null,"abstract":"International Classification of Diseases (ICD) coding is the task of assigning a patient’s electronic health records into standardized codes, which is crucial for enhancing medical services and reducing healthcare costs. In Korea, automatic Korean Standard Classification of Diseases (KCD) coding has been hindered by limited resources, differences in ICD systems, and language-specific characteristics. Therefore, we construct the Korean Dataset for Automatic KCD coding (KoDAK) by collecting and preprocessing Korean clinical documents. In addition, we propose a tokenization method optimized for Korean clinical documents. Our experiments show that our proposed method outperforms Korean Medical BERT (KM-BERT) in Macro-F1 performance by 0.14%p while using fewer model parameters, demonstrating its effectiveness in Korean clinical documents.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131064709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Transfer Learning for Low-Resource Clinical Named Entity Recognition","authors":"Nevasini Sasikumar, Krishna Sri Ipsit Mantri","doi":"10.18653/v1/2023.clinicalnlp-1.53","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.53","url":null,"abstract":"We propose a transfer learning method that adapts a high-resource English clinical NER model to low-resource languages and domains using only small amounts of in-domain annotated data. Our approach involves translating in-domain datasets to English, fine-tuning the English model on the translated data, and then transferring it to the target language/domain. Experiments on Spanish, French, and conversational clinical text datasets show accuracy gains over models trained on target data alone. Our method achieves state-of-the-art performance and can enable clinical NLP in more languages and modalities with limited resources.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130605482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Natsuki Murakami, Mana Ishida, Yuta Takahashi, Hitomi Yanaka, D. Bekki
{"title":"Knowledge Injection for Disease Names in Logical Inference between Japanese Clinical Texts","authors":"Natsuki Murakami, Mana Ishida, Yuta Takahashi, Hitomi Yanaka, D. Bekki","doi":"10.18653/v1/2023.clinicalnlp-1.14","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.14","url":null,"abstract":"In the medical field, there are many clinical texts such as electronic medical records, and research on Japanese natural language processing using these texts has been conducted.One such research involves Recognizing Textual Entailment (RTE) in clinical texts using a semantic analysis and logical inference system, ccg2lambda.However, it is difficult for existing inference systems to correctly determine the entailment relations , if the input sentence contains medical domain specific paraphrases such as disease names.In this study, we propose a method to supplement the equivalence relations of disease names as axioms by identifying candidates for paraphrases that lack in theorem proving.Candidates of paraphrases are identified by using a model for the NER task for disease names and a disease name dictionary.We also construct an inference test set that requires knowledge injection of disease names and evaluate our inference system.Experiments showed that our inference system was able to correctly infer for 106 out of 149 inference test sets.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123845167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Medical knowledge-enhanced prompt learning for diagnosis classification from clinical text","authors":"Yuxing Lu, Xukai Zhao, Jinzhuo Wang","doi":"10.18653/v1/2023.clinicalnlp-1.33","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.33","url":null,"abstract":"Artificial intelligence based diagnosis systems have emerged as powerful tools to reform traditional medical care. Each clinician now wants to have his own intelligent diagnostic partner to expand the range of services he can provide. When reading a clinical note, experts make inferences with relevant knowledge. However, medical knowledge appears to be heterogeneous, including structured and unstructured knowledge. Existing approaches are incapable of uniforming them well. Besides, the descriptions of clinical findings in clinical notes, which are reasoned to diagnosis, vary a lot for different diseases or patients. To address these problems, we propose a Medical Knowledge-enhanced Prompt Learning (MedKPL) model for diagnosis classification. First, to overcome the heterogeneity of knowledge, given the knowledge relevant to diagnosis, MedKPL extracts and normalizes the relevant knowledge into a prompt sequence. Then, MedKPL integrates the knowledge prompt with the clinical note into a designed prompt for representation. Therefore, MedKPL can integrate medical knowledge into the models to enhance diagnosis and effectively transfer learned diagnosis capacity to unseen diseases using alternating relevant disease knowledge. The experimental results on two medical datasets show that our method can obtain better medical text classification results and can perform better in transfer and few-shot settings among datasets of different diseases.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127809684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}