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International Workshop on Health Text Mining and Information Analysis最新文献

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Automatic Patient Note Assessment without Strong Supervision 自动病人笔记评估没有强有力的监督
International Workshop on Health Text Mining and Information Analysis Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.louhi-1.14
Jianing Zhou, Vyom Thakkar, R. Yudkowsky, S. Bhat, W. Bond
{"title":"Automatic Patient Note Assessment without Strong Supervision","authors":"Jianing Zhou, Vyom Thakkar, R. Yudkowsky, S. Bhat, W. Bond","doi":"10.18653/v1/2022.louhi-1.14","DOIUrl":"https://doi.org/10.18653/v1/2022.louhi-1.14","url":null,"abstract":"Training of physicians requires significant practice writing patient notes that document the patient’s medical and health information and physician diagnostic reasoning. Assessment and feedback of the patient note requires experienced faculty, consumes significant amounts of time and delays feedback to learners. Grading patient notes is thus a tedious and expensive process for humans that could be improved with the addition of natural language processing. However, the large manual effort required to create labeled datasets increases the challenge, particularly when test cases change. Therefore, traditional supervised NLP methods relying on labelled datasets are impractical in such a low-resource scenario. In our work, we proposed an unsupervised framework as a simple baseline and a weakly supervised method utilizing transfer learning for automatic assessment of patient notes under a low-resource scenario. Experiments on our self-collected datasets show that our weakly-supervised methods could provide reliable assessment for patient notes with accuracy of 0.92.","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","volume":"19 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":"132654446","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}
引用次数: 2
Building a Clinically-Focused Problem List From Medical Notes 从医疗记录中建立以临床为重点的问题清单
International Workshop on Health Text Mining and Information Analysis Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.louhi-1.8
Amir Feder, Itay Laish, Shashank Agarwal, U. Lerner, A. Atias, Cathy Cheung, P. Clardy, Alon Peled-Cohen, Rachana Fellinger, Hengrui Liu, Lan Huong Nguyen, Birju S. Patel, Natan Potikha, Amir Taubenfeld, Liwen Xu, Seung Doo Yang, Ayelet Benjamini, A. Hassidim
{"title":"Building a Clinically-Focused Problem List From Medical Notes","authors":"Amir Feder, Itay Laish, Shashank Agarwal, U. Lerner, A. Atias, Cathy Cheung, P. Clardy, Alon Peled-Cohen, Rachana Fellinger, Hengrui Liu, Lan Huong Nguyen, Birju S. Patel, Natan Potikha, Amir Taubenfeld, Liwen Xu, Seung Doo Yang, Ayelet Benjamini, A. Hassidim","doi":"10.18653/v1/2022.louhi-1.8","DOIUrl":"https://doi.org/10.18653/v1/2022.louhi-1.8","url":null,"abstract":"Clinical notes often contain useful information not documented in structured data, but their unstructured nature can lead to critical patient-related information being missed. To increase the likelihood that this valuable information is utilized for patient care, algorithms that summarize notes into a problem list have been proposed. Focused on identifying medically-relevant entities in the free-form text, these solutions are often detached from a canonical ontology and do not allow downstream use of the detected text-spans. Mitigating these issues, we present here a system for generating a canonical problem list from medical notes, consisting of two major stages. At the first stage, annotation, we use a transformer model to detect all clinical conditions which are mentioned in a single note. These clinical conditions are then grounded to a predefined ontology, and are linked to spans in the text. At the second stage, summarization, we develop a novel algorithm that aggregates over the set of clinical conditions detected on all of the patient’s notes, and produce a concise patient summary that organizes their most important conditions.","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","volume":"24 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":"114341992","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}
引用次数: 2
Enriching Deep Learning with Frame Semantics for Empathy Classification in Medical Narrative Essays 用框架语义丰富深度学习在医学叙事文章共情分类中的应用
International Workshop on Health Text Mining and Information Analysis Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.louhi-1.23
Priyanka Dey, Roxana Girju
{"title":"Enriching Deep Learning with Frame Semantics for Empathy Classification in Medical Narrative Essays","authors":"Priyanka Dey, Roxana Girju","doi":"10.18653/v1/2022.louhi-1.23","DOIUrl":"https://doi.org/10.18653/v1/2022.louhi-1.23","url":null,"abstract":"Empathy is a vital component of health care and plays a key role in the training of future doctors. Paying attention to medical students’ self-reflective stories of their interactions with patients can encourage empathy and the formation of professional identities that embody desirable values such as integrity and respect. We present a computational approach and linguistic analysis of empathic language in a large corpus of 440 essays written by pre-med students as narrated simulated patient – doctor interactions. We analyze the discourse of three kinds of empathy: cognitive, affective, and prosocial as highlighted by expert annotators. We also present various experiments with state-of-the-art recurrent neural networks and transformer models for classifying these forms of empathy. To further improve over these results, we develop a novel system architecture that makes use of frame semantics to enrich our state-of-the-art models. We show that this novel framework leads to significant improvement on the empathy classification task for this dataset.","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","volume":"29 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":"132673318","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}
引用次数: 3
A Knowledge-Graph-Based Intrinsic Test for Benchmarking Medical Concept Embeddings and Pretrained Language Models 基于知识图的医学概念嵌入和预训练语言模型的内在测试
International Workshop on Health Text Mining and Information Analysis Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.louhi-1.22
Claudio Aracena, F. Villena, Matías Rojas, J. Dunstan
{"title":"A Knowledge-Graph-Based Intrinsic Test for Benchmarking Medical Concept Embeddings and Pretrained Language Models","authors":"Claudio Aracena, F. Villena, Matías Rojas, J. Dunstan","doi":"10.18653/v1/2022.louhi-1.22","DOIUrl":"https://doi.org/10.18653/v1/2022.louhi-1.22","url":null,"abstract":"Using language models created from large data sources has improved the performance of several deep learning-based architectures, obtaining state-of-the-art results in several NLP extrinsic tasks. However, little research is related to creating intrinsic tests that allow us to compare the quality of different language models when obtaining contextualized embeddings. This gap increases even more when working on specific domains in languages other than English. This paper proposes a novel graph-based intrinsic test that allows us to measure the quality of different language models in clinical and biomedical domains in Spanish. Our results show that our intrinsic test performs better for clinical and biomedical language models than a general one. Also, it correlates with better outcomes for a NER task using a probing model over contextualized embeddings. We hope our work will help the clinical NLP research community to evaluate and compare new language models in other languages and find the most suitable models for solving downstream tasks.","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","volume":"134 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":"122856034","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}
引用次数: 3
Section Classification in Clinical Notes with Multi-task Transformers 多任务变压器在临床笔记中的分段分类
International Workshop on Health Text Mining and Information Analysis Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.louhi-1.7
Fan Zhang, Itay Laish, Ayelet Benjamini, Amir Feder
{"title":"Section Classification in Clinical Notes with Multi-task Transformers","authors":"Fan Zhang, Itay Laish, Ayelet Benjamini, Amir Feder","doi":"10.18653/v1/2022.louhi-1.7","DOIUrl":"https://doi.org/10.18653/v1/2022.louhi-1.7","url":null,"abstract":"Clinical notes are the backbone of electronic health records, often containing vital information not observed in other structured data. Unfortunately, the unstructured nature of clinical notes can lead to critical patient-related information being lost. Algorithms that organize clinical notes into distinct sections are often proposed in order to allow medical professionals to better access information in a given note. These algorithms, however, often assume a given partition over the note, and classify section types given this information. In this paper, we propose a multi-task solution for note sectioning, where a single model identifies context changes and labels each section with its medically-relevant title. Results on in-distribution (MIMIC-III) and out-of-distribution (private held-out) datasets reveal that our approach successfully identifies note sections across different hospital systems.","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","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":"124997141","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}
引用次数: 1
BioSimCSE: BioMedical Sentence Embeddings using Contrastive learning BioSimCSE:使用对比学习的生物医学句子嵌入
International Workshop on Health Text Mining and Information Analysis Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.louhi-1.10
Kamal Raj Kanakarajan, Bhuvana Kundumani, A. Abraham, Malaikannan Sankarasubbu
{"title":"BioSimCSE: BioMedical Sentence Embeddings using Contrastive learning","authors":"Kamal Raj Kanakarajan, Bhuvana Kundumani, A. Abraham, Malaikannan Sankarasubbu","doi":"10.18653/v1/2022.louhi-1.10","DOIUrl":"https://doi.org/10.18653/v1/2022.louhi-1.10","url":null,"abstract":"Sentence embeddings in the form of fixed-size vectors that capture the information in the sentence as well as the context are critical components of Natural Language Processing systems. With transformer model based sentence encoders outperforming the other sentence embedding methods in the general domain, we explore the transformer based architectures to generate dense sentence embeddings in the biomedical domain. In this work, we present BioSimCSE, where we train sentence embeddings with domain specific transformer based models with biomedical texts. We assess our model’s performance with zero-shot and fine-tuned settings on Semantic Textual Similarity (STS) and Recognizing Question Entailment (RQE) tasks. Our BioSimCSE model using BioLinkBERT achieves state of the art (SOTA) performance on both tasks.","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","volume":"15 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":"114893133","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}
引用次数: 2
Exploring the Influence of Dialog Input Format for Unsupervised Clinical Questionnaire Filling 探讨对话框输入格式对无监督临床问卷填写的影响
International Workshop on Health Text Mining and Information Analysis Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.louhi-1.1
Farnaz Ghassemi Toudeshki, Anna A. Liednikova, Ph. Jolivet, Claire Gardent
{"title":"Exploring the Influence of Dialog Input Format for Unsupervised Clinical Questionnaire Filling","authors":"Farnaz Ghassemi Toudeshki, Anna A. Liednikova, Ph. Jolivet, Claire Gardent","doi":"10.18653/v1/2022.louhi-1.1","DOIUrl":"https://doi.org/10.18653/v1/2022.louhi-1.1","url":null,"abstract":"In the medical field, we have seen the emergence of health-bots that interact with patients to gather data and track their state. One of the downstream application is automatic questionnaire filling, where the content of the dialog is used to automatically fill a pre-defined medical questionnaire. Previous work has shown that answering questions from the dialog context can successfully be cast as a Natural Language Inference (NLI) task and therefore benefit from current pre-trained NLI models. However, NLI models have mostly been trained on text rather than dialogs, which may have an influence on their performance. In this paper, we study the influence of content transformation and content selection on the questionnaire filling task. Our results demonstrate that dialog pre-processing can significantly improve the performance of zero-shot questionnaire filling models which take health-bots dialogs as input.","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","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":"131006672","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}
引用次数: 0
Distinguishing between focus and background entities in biomedical corpora using discourse structure and transformers 用语篇结构和变形法区分生物医学语料库中的焦点实体和背景实体
International Workshop on Health Text Mining and Information Analysis Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.louhi-1.4
Antonio José Jimeno Yepes, Karin M. Verspoor
{"title":"Distinguishing between focus and background entities in biomedical corpora using discourse structure and transformers","authors":"Antonio José Jimeno Yepes, Karin M. Verspoor","doi":"10.18653/v1/2022.louhi-1.4","DOIUrl":"https://doi.org/10.18653/v1/2022.louhi-1.4","url":null,"abstract":"Scientific documents typically contain numerous entity mentions, while only a subset are directly relevant to the key contributions of the paper. Distinguishing these focus entities from background ones effectively could improve the recovery of relevant documents and the extraction of information from documents. To study the identification of focus entities, we developed two large datasets of disease-causing biological pathogens using MEDLINE, the largest collection of biomedical citations, and PubMed Central, a collection of full text articles. The focus entities were identified using human-curated indexing on these collections. Experiments with machine learning methods to identify focus entities show that transformer methods achieve high precision and recall and that document discourse information is relevant. The work lays the foundation for more targeted retrieval/summarisation of entity-relevant documents.","PeriodicalId":448872,"journal":{"name":"International Workshop on Health Text Mining and Information Analysis","volume":"44 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":"114990804","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}
引用次数: 0
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