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Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes. 问题列表总结概述(ProbSum) 2023关于总结患者主动诊断和电子健康记录进度记录问题的共享任务。

Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2023-07-01 DOI: 10.18653/v1/2023.bionlp-1.43

Yanjun Gao, Dmitriy Dligach, Timothy Miller, Matthew M Churpek, Majid Afshar

{"title":"Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes.","authors":"Yanjun Gao, Dmitriy Dligach, Timothy Miller, Matthew M Churpek, Majid Afshar","doi":"10.18653/v1/2023.bionlp-1.43","DOIUrl":"https://doi.org/10.18653/v1/2023.bionlp-1.43","url":null,"abstract":"The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023. The aim of this shared task is to attract future research efforts in building NLP models for real-world diagnostic decision support applications, where a system generating relevant and accurate diagnoses will augment the healthcare providers' decision-making process and improve the quality of care for patients. The goal for participants is to develop models that generated a list of diagnoses and problems using input from the daily care notes collected from the hospitalization of critically ill patients. Eight teams submitted their final systems to the shared task leaderboard. In this paper, we describe the tasks, datasets, evaluation metrics, and baseline systems. Additionally, the techniques and results of the evaluation of the different approaches tried by the participating teams are summarized.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2023 ","pages":"461-467"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10426335/pdf/nihms-1923203.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10017111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

End-to-end clinical temporal information extraction with multi-head attention. 使用多头注意力进行端到端临床时间信息提取。

Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2023-07-01

Timothy Miller, Steven Bethard, Dmitriy Dligach, Guergana Savova

引用次数: 0

Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning. 基于领域内语言模型的诊断推理多任务训练。

Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2023-07-01

Brihat Sharma, Yanjun Gao, Timothy Miller, Matthew M Churpek, Majid Afshar, Dmitriy Dligach

引用次数: 0

End-to-end clinical temporal information extraction with multi-head attention 基于多头注意力的端到端临床时间信息提取

Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2023-07-01 DOI: 10.18653/v1/2023.bionlp-1.28

Timothy Miller, S. Bethard, Dmitriy Dligach, G. Savova

引用次数: 0

Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients’ Active Diagnoses and Problems from Electronic Health Record Progress Notes 问题列表总结概述(ProbSum) 2023关于总结患者主动诊断和电子健康记录进度记录问题的共享任务

Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2023-06-08 DOI: 10.48550/arXiv.2306.05270

Yanjun Gao, Dmitriy Dligach, Timothy Miller, M. Churpek, M. Afshar

引用次数: 6

Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning 基于领域内语言模型的诊断推理多任务训练

Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2023-06-07 DOI: 10.48550/arXiv.2306.04551

B. Sharma, Yanjun Gao, Timothy Miller, M. Churpek, M. Afshar, Dmitriy Dligach

引用次数: 1

Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses 不太可能的头脑风暴:使用语言模型生成替代假设

Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2023-05-30 DOI: 10.48550/arXiv.2305.19339

Liyan Tang, Yifan Peng, Yanshan Wang, Ying Ding, Greg Durrett, Justin F. Rousseau

{"title":"Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses","authors":"Liyan Tang, Yifan Peng, Yanshan Wang, Ying Ding, Greg Durrett, Justin F. Rousseau","doi":"10.48550/arXiv.2305.19339","DOIUrl":"https://doi.org/10.48550/arXiv.2305.19339","url":null,"abstract":"A human decision-maker benefits the most from an AI assistant that corrects for their biases. For problems such as generating interpretation of a radiology report given findings, a system predicting only highly likely outcomes may be less useful, where such outcomes are already obvious to the user. To alleviate biases in human decision-making, it is worth considering a broad differential diagnosis, going beyond the most likely options. We introduce a new task, \"less likely brainstorming,\" that asks a model to generate outputs that humans think are relevant but less likely to happen. We explore the task in two settings: a brain MRI interpretation generation setting and an everyday commonsense reasoning setting. We found that a baseline approach of training with less likely hypotheses as targets generates outputs that humans evaluate as either likely or irrelevant nearly half of the time; standard MLE training is not effective. To tackle this problem, we propose a controlled text generation method that uses a novel contrastive learning strategy to encourage models to differentiate between generating likely and less likely outputs according to humans. We compare our method with several state-of-the-art controlled text generation models via automatic and human evaluations and show that our models' capability of generating less likely outputs is improved.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"1 1","pages":"12532-12555"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83485059","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

Revisiting Relation Extraction in the era of Large Language Models 再论大语言模型时代的关系抽取

Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2023-05-08 DOI: 10.48550/arXiv.2305.05003

Somin Wadhwa, Silvio Amir, Byron C. Wallace

{"title":"Revisiting Relation Extraction in the era of Large Language Models","authors":"Somin Wadhwa, Silvio Amir, Byron C. Wallace","doi":"10.48550/arXiv.2305.05003","DOIUrl":"https://doi.org/10.48550/arXiv.2305.05003","url":null,"abstract":"Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them. Recent work has instead treated the problem as a sequence-to-sequence task, linearizing relations between entities as target strings to be generated conditioned on the input. Here we push the limits of this approach, using larger language models (GPT-3 and Flan-T5 large) than considered in prior work and evaluating their performance on standard RE tasks under varying levels of supervision. We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching. Under this refined evaluation, we find that: (1) Few-shot prompting with GPT-3 achieves near SOTA performance, i.e., roughly equivalent to existing fully supervised models; (2) Flan-T5 is not as capable in the few-shot setting, but supervising and fine-tuning it with Chain-of-Thought (CoT) style explanations (generated via GPT-3) yields SOTA results. We release this model as a new baseline for RE tasks.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"256 1","pages":"15566-15589"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89059389","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: 13

Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges. 自动总结临床试验证据:一个突出当前挑战的原型。

Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2023-05-01

Sanjana Ramprasad, Iain J Marshall, Denis Jered McInerney, Byron C Wallace

{"title":"Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges.","authors":"Sanjana Ramprasad, Iain J Marshall, Denis Jered McInerney, Byron C Wallace","doi":"","DOIUrl":"","url":null,"abstract":"We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query. Building on prior work (Marshall et al., 2020), the system retrieves trial publications matching a query specifying a combination of condition, intervention(s), and outcome(s), and ranks these according to sample size and estimated study quality. The top-k such studies are passed through a neural multi-document summarization system, yielding a synopsis of these trials. We consider two architectures: A standard sequence-to-sequence model based on BART (Lewis et al., 2019), and a multi-headed architecture intended to provide greater transparency to end-users. Both models produce fluent and relevant summaries of evidence retrieved for queries, but their tendency to introduce unsupported statements render them inappropriate for use in this domain at present. The proposed architecture may help users verify outputs allowing users to trace generated tokens back to inputs. The demonstration video is available at: https://vimeo.com/735605060 The prototype, source code, and model weights are available at: https://sanjanaramprasad.github.io/trials-summarizer/.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2023 ","pages":"236-247"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361334/pdf/nihms-1912129.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10240091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IRMA: the 335-million-word Italian coRpus for studying MisinformAtion. IRMA: 3.35亿字的意大利语语料库，用于研究错误信息。

Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2023-05-01

Fabio Carrella, Alessandro Miani, Stephan Lewandowsky

{"title":"IRMA: the 335-million-word Italian coRpus for studying MisinformAtion.","authors":"Fabio Carrella, Alessandro Miani, Stephan Lewandowsky","doi":"","DOIUrl":"","url":null,"abstract":"The dissemination of false information on the internet has received considerable attention over the last decade. Misinformation often spreads faster than mainstream news, thus making manual fact checking inefficient or, at best, labor-intensive. Therefore, there is an increasing need to develop methods for automatic detection of misinformation. Although resources for creating such methods are available in English, other languages are often underrepresented in this effort. With this contribution, we present IRMA, a corpus containing over 600,000 Italian news articles (335+ million tokens) collected from 56 websites classified as 'untrustworthy' by professional factcheckers. The corpus is freely available and comprises a rich set of text- and website-level data, representing a turnkey resource to test hypotheses and develop automatic detection algorithms. It contains texts, titles, and dates (from 2004 to 2022), along with three types of semantic measures (i.e., keywords, topics at three different resolutions, and LIWC lexical features). IRMA also includes domainspecific information such as source type (e.g., political, health, conspiracy, etc.), quality, and higher-level metadata, including several metrics of website incoming traffic that allow to investigate user online behavior. IRMA constitutes the largest corpus of misinformation available today in Italian, making it a valid tool for advancing quantitative research on untrustworthy news detection and ultimately helping limit the spread of misinformation.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2023 ","pages":"2339-2349"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138300729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0