Jeremy Weiss


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AnEMIC: A Framework for Benchmarking ICD Coding Models
Juyong Kim | Abheesht Sharma | Suhas Shanbhogue | Jeremy Weiss | Pradeep Ravikumar
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Diagnostic coding, or ICD coding, is the task of assigning diagnosis codes defined by the ICD (International Classification of Diseases) standard to patient visits based on clinical notes. The current process of manual ICD coding is time-consuming and often error-prone, which suggests the need for automatic ICD coding. However, despite the long history of automatic ICD coding, there have been no standardized frameworks for benchmarking ICD coding models. We open-source an easy-to-use tool named AnEMIC, which provides a streamlined pipeline for preprocessing, training, and evaluating for automatic ICD coding. We correct errors in preprocessing by existing works, and provide key models and weights trained on the correctly preprocessed datasets. We also provide an interactive demo performing real-time inference from custom inputs, and visualizations drawn from explainable AI to analyze the models. We hope the framework helps move the research of ICD coding forward and helps professionals explore the potential of ICD coding. The framework and the associated code are available here.


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ESPRIT: Explaining Solutions to Physical Reasoning Tasks
Nazneen Fatema Rajani | Rui Zhang | Yi Chern Tan | Stephan Zheng | Jeremy Weiss | Aadit Vyas | Abhijit Gupta | Caiming Xiong | Richard Socher | Dragomir Radev
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. We use a two-step approach of first identifying the pivotal physical events in an environment and then generating natural language descriptions of those events using a data-to-text approach. Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions. Human evaluations indicate that ESPRIT produces crucial fine-grained details and has high coverage of physical concepts compared to even human annotations. Dataset, code and documentation are available at