Papers by David Liem
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)
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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
| Challenge: | a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications . |
| Approach: | a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19. |
| Outcome: | a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing . |
EVIDENCEMINER: Textual Evidence Discovery for Life Sciences (2020.acl-demos)
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Xuan Wang, Yingjun Guan, Weili Liu, Aabhas Chauhan, Enyi Jiang, Qi Li, David Liem, Dibakar Sigdel, John Caufield, Peipei Ping, Jiawei Han
| Challenge: | EVIDENCEMINER is a web-based system that allows users to query a natural language statement and retrieve textual evidence from a background corpora for life sciences. |
| Approach: | They propose a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. |
| Outcome: | EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. |
DRGCoder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups (2023.emnlp-demo)
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Daniel Hajialigol, Derek Kaknes, Tanner Barbour, Daphne Yao, Chris North, Jimeng Sun, David Liem, Xuan Wang
| Challenge: | Medical claim coding is the process of transforming medical records into structured codes in a classification system such as ICD-10 (International Classification of Diseases, Tenth Revision) or DRG (Diagnosis-Related Group) codes. |
| Approach: | They propose an explainability-enhanced clinical claim coding system for the early prediction of medical severity DRGs (MS-DRGs) a novel multi-task Transformer model allows users to inspect DRGCoder’s reasoning by visualizing the weights for each word of the input. |
| Outcome: | The proposed system allows users to analyze the weights of the input and compare across multiple discharge summaries. |