David Liem


2023

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DRGCoder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups
Daniel Hajialigol | Derek Kaknes | Tanner Barbour | Daphne Yao | Chris North | Jimeng Sun | David Liem | Xuan Wang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Medical claim coding is the process of transforming medical records, usually presented as free texts written by clinicians, or discharge summaries, into structured codes in a classification system such as ICD-10 (International Classification of Diseases, Tenth Revision) or DRG (Diagnosis-Related Group) codes. This process is essential for medical billing and transitional care; however, manual coding is time-consuming, error-prone, and expensive. To solve these issues, we propose DRGCoder, an explainability-enhanced clinical claim coding system for the early prediction of medical severity DRGs (MS-DRGs), a classification system that categorizes patients’ hospital stays into various DRG groups based on the severity of illness and mortality risk. The DRGCoder framework introduces a novel multi-task Transformer model for MS-DRG prediction, modeling both the DRG labels of the discharge summaries and the important, or salient words within he discharge summaries. We allow users to inspect DRGCoder’s reasoning by visualizing the weights for each word of the input. Additionally, DRGCoder allows users to identify diseases within discharge summaries and compare across multiple discharge summaries. Our demo is available at https://huggingface.co/spaces/danielhajialigol/DRGCoder. A video demonstrating the demo can be found at https://www.youtube.com/watch?v=pcdiG6VwqlA

2021

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COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Qingyun Wang | Manling Li | Xuan Wang | Nikolaus Parulian | Guangxing Han | Jiawei Ma | Jingxuan Tu | Ying Lin | Ranran Haoran Zhang | Weili Liu | Aabhas Chauhan | Yingjun Guan | Bangzheng Li | Ruisong Li | Xiangchen Song | Yi Fung | Heng Ji | Jiawei Han | Shih-Fu Chang | James Pustejovsky | Jasmine Rah | David Liem | Ahmed ELsayed | Martha Palmer | Clare Voss | Cynthia Schneider | Boyan Onyshkevych
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports.

2020

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EVIDENCEMINER: Textual Evidence Discovery for Life Sciences
Xuan Wang | Yingjun Guan | Weili Liu | Aabhas Chauhan | Enyi Jiang | Qi Li | David Liem | Dibakar Sigdel | John Caufield | Peipei Ping | Jiawei Han
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Traditional search engines for life sciences (e.g., PubMed) are designed for document retrieval and do not allow direct retrieval of specific statements. Some of these statements may serve as textual evidence that is key to tasks such as hypothesis generation and new finding validation. We present EVIDENCEMINER, a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. EVIDENCEMINER is constructed in a completely automated way without any human effort for training data annotation. It is supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction. The entities and patterns are pre-computed and indexed offline to support fast online evidence retrieval. The annotation results are also highlighted in the original document for better visualization. EVIDENCEMINER also includes analytic functionalities such as the most frequent entity and relation summarization. EVIDENCEMINER can help scientists uncover important research issues, leading to more effective research and more in-depth quantitative analysis. The system of EVIDENCEMINER is available at https://evidenceminer.firebaseapp.com/.

2017

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Life-iNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences
Xiang Ren | Jiaming Shen | Meng Qu | Xuan Wang | Zeqiu Wu | Qi Zhu | Meng Jiang | Fangbo Tao | Saurabh Sinha | David Liem | Peipei Ping | Richard Weinshilboum | Jiawei Han
Proceedings of ACL 2017, System Demonstrations