Papers by James Mullenbach
Explainable Prediction of Medical Codes from Clinical Text (N18-1)
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| Challenge: | Clinical notes are text documents that are created by clinicians for each patient encounter. |
| Approach: | They propose a method that aggregates information across the document using a convolutional neural network and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. |
| Outcome: | The proposed method is accurate and better than the current state of the art. |
Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects (D19-1)
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| Challenge: | a dataset of human judgments is used to test the ability to construct models with an understanding of commonsense knowledge. |
| Approach: | They crowdsource sentences that answer a question about adjectives and their transitivity . they build strong baselines for the task using a classification approach . |
| Outcome: | The proposed model outperforms word-level models on commonsense reasoning tasks. |
CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes (2021.acl-long)
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James Mullenbach, Yada Pruksachatkun, Sean Adler, Jennifer Seale, Jordan Swartz, Greg McKelvey, Hui Dai, Yi Yang, David Sontag
| Challenge: | Continuity of care is crucial to ensuring positive health outcomes for patients discharged from an inpatient hospital setting. |
| Approach: | They propose to annotate clinical action items from a dataset of medical notes annotated by physicians and extract them as multi-aspect extractive summarization. |
| Outcome: | The proposed dataset is annotated by physicians and covers 718 documents representing 100K sentences. |