Papers by Ramakanth Kavuluru
EMR Coding with Semi-Parametric Multi-Head Matching Networks (N18-1)
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| Challenge: | Electronic medical record (EMR) coding is the process of extracting diagnosis and procedure codes from the digital record (the EMR) pertaining to a patient's visit. |
| Approach: | They propose a neural network architecture that combines ideas from few-shot learning matching networks, multi-label loss functions, and convolutional neural networks for text classification to significantly outperform other state-of-the-art models. |
| Outcome: | The proposed model outperforms existing models on a well known de-identified EMR dataset with multi-label performance measures. |
Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization (2020.findings-emnlp)
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| Challenge: | IR for precision medicine often involves looking for multiple pieces of evidence that characterize a patient case. |
| Approach: | They propose a document reranking approach that combines neural query-document matching and text summarization toward such retrieval scenarios. |
| Outcome: | The proposed approach achieves state-of-the-art performance on NIST's TREC-PM track dataset. |
Knowledge-Driven Cross-Document Relation Extraction (2024.findings-acl)
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| Challenge: | Existing approaches to extract relationships between entities are based on sentence-level tasks, but they do not consider domain knowledge, which are assumed to be known to the reader when documents are authored. |
| Approach: | They propose to embed domain knowledge of entities with input text for cross-document RE by embedding domain knowledge with the document. |
| Outcome: | The proposed framework offers interpretability by producing explanatory text for predicted relations between entities and improves performance over baseline methods. |
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces (D18-1)
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| Challenge: | Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group) and labels that never appear in the training dataset (zero-shot groups). |
| Approach: | They perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. |
| Outcome: | The proposed methods improve on two publicly available datasets for multi-label text classification. |