Papers by Kirk Roberts
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries (2022.lrec-1)
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| Challenge: | a new question answering dataset is being developed for electronic health records . structured tables and unstructured notes can be duplicated, contradictory or provide additional context . |
| Approach: | They develop a question-answer-matching dataset using structured tables and unstructured notes from an EHR. |
| Outcome: | The proposed model is based on a model with a modality selection network . it uses the prediction of a RAT-SQL to choose between EHR tables and clinical notes . |
A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports (2022.lrec-1)
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| Challenge: | Oftentimes, these findings and devices are referred to multiple times in a single report and are also referred across different reports of a patient. |
| Approach: | They propose a new cross-document coreference resolution (CDCR) dataset for identifying co-referring radiological findings and medical devices across a patient's radiology reports. |
| Outcome: | The proposed dataset contains 5872 mentions (findings and devices) spanning 638 MIMIC-III radiology reports across 60 patients, covering multiple imaging modalities and anatomies. |
Extracting Adherence Information from Electronic Health Records (2020.coling-main)
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Jordan Sanders, Meghana Gudala, Kathleen Hamilton, Nishtha Prasad, Jordan Stovall, Eduardo Blanco, Jane E Hamilton, Kirk Roberts
| Challenge: | adherence is a critical factor in health outcomes, and is often modeled as a binary decision . adherence models include intentional and unintentional non-adherence, social support and other patient attributes such as age and time since diagnosis. |
| Approach: | They propose to extract adherence information from electronic health records using de-identified sentences and a corpus of 3,000 de-identified sentences. |
| Outcome: | The proposed framework extracts medication adherence information from electronic health records. |
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering (2020.lrec-1)
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| Challenge: | Existing work on EHR QA models that learn to answer questions from structured data has focused on analyzing questions or mapping questions to existing NLP based information extraction models. |
| Approach: | They conduct 48 experiments on two clinical question answering datasets . they use open-domain and domain-specific corpora to fine-tune Transformer language models . |
| Outcome: | The proposed models can learn to answer questions from unstructured notes with accuracies up to 90% on open-domain and domain-specific corpora. |
A FrameNet for Cancer Information in Clinical Narratives: Schema and Annotation (L18-1)
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| Challenge: | Existing natural language processing (NLP) systems for cancer-related information are highly task-specific and often produce incompatible annotations and algorithms. |
| Approach: | They propose a general-purpose natural language processing resource for cancer-related information in clinical notes . the project uses a frame semantic method to emphasize the information presented in the notes themselves . |
| Outcome: | The proposed project emphasizes the information presented in the clinical notes and its linguistic structure. |
Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports (2020.lrec-1)
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Surabhi Datta, Morgan Ulinski, Jordan Godfrey-Stovall, Shekhar Khanpara, Roy F. Riascos-Castaneda, Kirk Roberts
| Challenge: | Existing framework for encoding spatial language in radiology is based on frame semantics . |
| Approach: | They propose a framework for encoding spatial language in radiology based on frame semantics and a corpus of 400 radiology reports annotated with spatial trigger expressions and contextual information. |
| Outcome: | The proposed framework is based on the existing SpatialNet representation in the general domain and is able to generate more accurate representations of spatial language in radiology. |
RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports (2022.lrec-1)
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| Challenge: | Question answering (QA) is an intuitive means to query text data. |
| Approach: | They propose a radiology question-answer-evidence-pair dataset with 3074 questions posed against radiology reports and annotated with their corresponding answer spans by physicians. |
| Outcome: | The proposed dataset has 3074 questions posed against radiology reports and annotated with their corresponding answer spans by physicians. |