Papers with doctor
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods (N18-2)
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| Challenge: | Existing methods for co-reference resolution focus on gender bias. |
| Approach: | They propose a new benchmark for co-reference resolution focused on gender bias, WinoBias. |
| Outcome: | The proposed system removes the bias without significantly affecting performance on existing datasets. |
Towards Generating Personalized Hospitalization Summaries (N18-4)
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Sabita Acharya, Barbara Di Eugenio, Andrew Boyd, Richard Cameron, Karen Dunn Lopez, Pamela Martyn-Nemeth, Carolyn Dickens, Amer Ardati
| Challenge: | 80% of the medical concepts that are considered important by both doctor and nurse are not included in the summaries provided to patients . |
| Approach: | They propose to combine information from discharge notes and nursing plan of care to generate personalized hospital-stay summaries for patients. |
| Outcome: | The summaries contain 80% of the medical concepts that are considered important by both doctor and nurses. |
Audio De-identification - a New Entity Recognition Task (N19-2)
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Ido Cohn, Itay Laish, Genady Beryozkin, Gang Li, Izhak Shafran, Idan Szpektor, Tzvika Hartman, Avinatan Hassidim, Yossi Matias
| Challenge: | Named Entity Recognition (NER) is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. |
| Approach: | They propose to use Named Entity Recognition (NER) to detect audio spans with entity mentions in medical records and then use it to evaluate the results. |
| Outcome: | The proposed pipeline is based on a large labeled segment of the Switchboard and Fisher audio datasets and compares it with a benchmark. |
The Clinical Fingerprint: Comparing the Rhetorical Integrity and Epistemic Safety of Human Physicians and Large Language Models (2026.eacl-srw)
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| Challenge: | Large Language Models demonstrate expert proficiency on medical benchmarks, but clinical encounter requires a sophisticated rhetorical performance of care. |
| Approach: | They compare the rhetorical performance of large language models with human physicians . they find that generic models often bury critical advice under layers of linguistic recursion . |
| Outcome: | The proposed models lack the ethical integrity needed to deliver clinical advice, the authors argue . they show that generic models often bury critical advice under layers of complex linguistic recursion . |
Doctor Recommendation in Online Health Forums via Expertise Learning (2022.acl-long)
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| Challenge: | Currently, manual doctor allocations are used to handle large volumes of queries, limiting the efficiency to help patients in sheer quantities. |
| Approach: | They propose to use patient queries to model doctor recommendation using their profiles and past dialogues to estimate their capabilities. |
| Outcome: | The proposed model outperforms baseline models on a Chinese online health forum, outperforming baseline models. |
The Brain-IHM Dataset: a New Resource for Studying the Brain Basis of Human-Human and Human-Machine Conversations (2020.lrec-1)
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| Challenge: | Using a dataset of controlled interactions, we have studied the feedback items produced by the interlocutors during a conversation. |
| Approach: | They propose to use a dataset of controlled interactions to study feedback items and a virtual reality context to re-synthesize the conversations. |
| Outcome: | The proposed dataset compares human-human and human-machine production of feedbacks and is the first of its kind. |
Stigma Annotation Scheme and Stigmatized Language Detection in Health-Care Discussions on Social Media (2020.lrec-1)
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| Challenge: | a large amount of research has been done on the interpretation and influence of stigma on human behaviour and health. |
| Approach: | They develop an annotation scheme and improve the annotation process for stigma identification . they aim to distinguish stigmatised language from non-stigmatised using machine learning and NLP . |
| Outcome: | The proposed method improves the annotation process for stigma identification . the results show that the method performs better than other models . |
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction (2020.emnlp-main)
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| Challenge: | Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction. |
| Approach: | They propose a new revision task that debiases text through the lens of connotation frames to correct implicit biases in character portrayals. |
| Outcome: | The proposed approach outperforms existing methods and ablations in the literature. |
Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation (2023.findings-emnlp)
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| Challenge: | Existing approaches ignore relationships between medical items and statuses in the multi-turn doctor-patient dialogue. |
| Approach: | They propose a task to extract structured medical information from free text dialogues . they propose 'Dialogue Medical Information Extraction' to model relationships between items . |
| Outcome: | The proposed model outperforms previous models and achieves state-of-the-art performance on the public benchmark data set. |