Papers by Dmitriy Dligach
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding (2022.lrec-1)
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Yanjun Gao, Dmitriy Dligach, Timothy Miller, Samuel Tesch, Ryan Laffin, Matthew M. Churpek, Majid Afshar
| Challenge: | Existing corpus and annotations focus on textual features and relation prediction, but there are no structured corpus models for clinical diagnostic thinking. |
| Approach: | They propose a hierarchical annotation schema with three stages to address clinical diagnostic thinking. |
| Outcome: | The proposed model is based on a large collection of publicly available daily progress notes. |
Summarizing Patients’ Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models (2022.coling-1)
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| Challenge: | Problem list summarization requires a model to understand, abstract, and generate clinical documentation. |
| Approach: | They propose a task that summarises patients' main problems from daily progress notes using input from the provider's progress notes during hospitalization. |
| Outcome: | The proposed model outperforms two state-of-the-art seq2seq transformer architectures in summarizing patients' main problems from daily progress notes in the medical information mart for Intensive Care (MIMIC)-III. |
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications? (2024.findings-emnlp)
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Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy Miller, Danielle Bitterman, Matthew Churpek, Majid Afshar
| Challenge: | Numerical data is pivotal for medical questions and answers, but tabular data is not fully integrated into LLMs. |
| Approach: | They examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record data. |
| Outcome: | The proposed representations outperform those using raw numerical EHR data in medical diagnostics and prognostics. |
LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval (2026.acl-long)
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He Cheng, Yifu Wu, Saksham Khatwani, Maya Kruse, Dmitriy Dligach, Timothy A. Miller, Majid Afshar, Yanjun Gao
| Challenge: | Existing systems struggle to balance efficiency, scalability, and interpretability. |
| Approach: | They propose a hardware-aligned framework that enables scalable and interpretable k-hop retrieval on large KGs. |
| Outcome: | The proposed framework scales to billion-edge graphs without loss of retrieval fidelity. |