Jason Fries


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Efficient Diagnosis Assignment Using Unstructured Clinical Notes
Louis Blankemeier | Jason Fries | Robert Tinn | Joseph Preston | Nigam Shah | Akshay Chaudhari
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Electronic phenotyping entails using electronic health records (EHRs) to identify patients with specific health outcomes and determine when those outcomes occurred. Unstructured clinical notes, which contain a vast amount of information, are a valuable resource for electronic phenotyping. However, traditional methods, such as rule-based labeling functions or neural networks, require significant manual effort to tune and may not generalize well to multiple indications. To address these challenges, we propose HyDE (hybrid diagnosis extractor). HyDE is a simple framework for electronic phenotyping that integrates labeling functions and a disease-agnostic neural network to assign diagnoses to patients. By training HyDE’s model to correct predictions made by labeling functions, we are able to disambiguate hypertension true positives and false positives with a supervised area under the precision-recall curve (AUPRC) of 0.85. We extend this hypertension-trained model to zero-shot evaluation of four other diseases, generating AUPRC values ranging from 0.82 - 0.95 and outperforming a labeling function baseline by 44 points in F1 score and a Word2Vec baseline by 24 points in F1 score on average. Furthermore, we demonstrate a speedup of >4x by pruning the length of inputs into our language model to ~2.3% of the full clinical notes, with negligible impact to the AUPRC. HyDE has the potential to improve the efficiency and efficacy of interpreting large-scale unstructured clinical notes for accurate EHR phenotyping.


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Dataset Debt in Biomedical Language Modeling
Jason Fries | Natasha Seelam | Gabriel Altay | Leon Weber | Myungsun Kang | Debajyoti Datta | Ruisi Su | Samuele Garda | Bo Wang | Simon Ott | Matthias Samwald | Wojciech Kusa
Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models

Large-scale language modeling and natural language prompting have demonstrated exciting capabilities for few and zero shot learning in NLP. However, translating these successes to specialized domains such as biomedicine remains challenging, due in part to biomedical NLP’s significant dataset debt – the technical costs associated with data that are not consistently documented or easily incorporated into popular machine learning frameworks at scale. To assess this debt, we crowdsourced curation of datasheets for 167 biomedical datasets. We find that only 13% of datasets are available via programmatic access and 30% lack any documentation on licensing and permitted reuse. Our dataset catalog is available at: https://tinyurl.com/bigbio22.

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PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
Stephen Bach | Victor Sanh | Zheng Xin Yong | Albert Webson | Colin Raffel | Nihal V. Nayak | Abheesht Sharma | Taewoon Kim | M Saiful Bari | Thibault Fevry | Zaid Alyafeai | Manan Dey | Andrea Santilli | Zhiqing Sun | Srulik Ben-david | Canwen Xu | Gunjan Chhablani | Han Wang | Jason Fries | Maged Al-shaibani | Shanya Sharma | Urmish Thakker | Khalid Almubarak | Xiangru Tang | Dragomir Radev | Mike Tian-jian Jiang | Alexander Rush
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target output. Using prompts to train and query language models is an emerging area in NLP that requires new tools that let users develop and refine these prompts collaboratively. PromptSource addresses the emergent challenges in this new setting with (1) a templating language for defining data-linked prompts, (2) an interface that lets users quickly iterate on prompt development by observing outputs of their prompts on many examples, and (3) a community-driven set of guidelines for contributing new prompts to a common pool. Over 2,000 prompts for roughly 170 datasets are already available in PromptSource. PromptSource is available at https://github.com/bigscience-workshop/promptsource.


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Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction
Jason Fries
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)