Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems

• URL: http://arxiv.org/abs/2310.10571v1
• Date: Mon, 16 Oct 2023 16:45:52 GMT
• Title: Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems
• Authors: Sagi Shaier, Kevin Bennett, Lawrence Hunter, Katharina von der Wense
• Abstract summary: We find that irrelevant demographic information change up to 15% of the answers of a KG-grounded system and up to 23% of the answers of a text-based system. We conclude that unjustified answer changes caused by patient demographics are a frequent phenomenon, which raises fairness concerns and should be paid more attention to.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art question answering (QA) models exhibit a variety of social biases (e.g., with respect to sex or race), generally explained by similar issues in their training data. However, what has been overlooked so far is that in the critical domain of biomedicine, any unjustified change in model output due to patient demographics is problematic: it results in the unfair treatment of patients. Selecting only questions on biomedical topics whose answers do not depend on ethnicity, sex, or sexual orientation, we ask the following research questions: (RQ1) Do the answers of QA models change when being provided with irrelevant demographic information? (RQ2) Does the answer of RQ1 differ between knowledge graph (KG)-grounded and text-based QA systems? We find that irrelevant demographic information change up to 15% of the answers of a KG-grounded system and up to 23% of the answers of a text-based system, including changes that affect accuracy. We conclude that unjustified answer changes caused by patient demographics are a frequent phenomenon, which raises fairness concerns and should be paid more attention to.

Related papers

• Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning [45.746882253686856]
  Medical visual question answering (VQA) aims to answer clinically relevant questions regarding input medical images. We first collected a comprehensive and large-scale medical VQA dataset, focusing on chest X-ray images. Based on this dataset, we also propose a novel baseline method by constructing three different relationship graphs.
  arXiv  Detail & Related papers  (2023-02-19T17:46:16Z)
• Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision [53.692793122749414]
  We introduce a medical question understanding and answering system with knowledge grounding and semantic self-supervision. Our system is a pipeline that first summarizes a long, medical, user-written question, using a supervised summarization loss. The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document.
  arXiv  Detail & Related papers  (2022-09-30T08:20:32Z)
• Learning to Ask Like a Physician [24.15961995052862]
  We present Discharge Summary Clinical Questions (DiSCQ), a newly curated question dataset composed of 2,000+ questions. The questions are generated by medical experts from 100+ MIMIC-III discharge summaries. We analyze this dataset to characterize the types of information sought by medical experts.
  arXiv  Detail & Related papers  (2022-06-06T15:50:54Z)
• Invariant Grounding for Video Question Answering [72.87173324555846]
  Video Question Answering (VideoQA) is the task of answering questions about a video. In leading VideoQA models, the typical learning objective, empirical risk minimization (ERM), latches on superficial correlations between video-question pairs and answers. We propose a new learning framework, Invariant Grounding for VideoQA (IGV), to ground the question-critical scene.
  arXiv  Detail & Related papers  (2022-06-06T04:37:52Z)
• Gender and Racial Bias in Visual Question Answering Datasets [24.075869811508404]
  We investigate gender and racial bias in visual question answering (VQA) datasets. We find that the distribution of answers is highly different between questions about women and men, as well as the existence of detrimental gender-stereotypical samples. Our findings suggest that there are dangers associated to using VQA datasets without considering and dealing with the potentially harmful stereotypes.
  arXiv  Detail & Related papers  (2022-05-17T07:33:24Z)
• Medical Visual Question Answering: A Survey [55.53205317089564]
  Medical Visual Question Answering(VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer.
  arXiv  Detail & Related papers  (2021-11-19T05:55:15Z)
• Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management [5.044336341666555]
  We introduce Q-Pain, a dataset for assessing bias in medical QA in the context of pain management. We propose a new, rigorous framework, including a sample experimental design, to measure the potential biases present when making treatment decisions.
  arXiv  Detail & Related papers  (2021-08-03T21:55:28Z)
• Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation Embedding [140.5911760063681]
  We propose a novel dataset named Knowledge-Routed Visual Question Reasoning for VQA model evaluation. We generate the question-answer pair based on both the Visual Genome scene graph and an external knowledge base with controlled programs.
  arXiv  Detail & Related papers  (2020-12-14T00:33:44Z)
• Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing [20.117014315684287]
  We use a taxonomy of Knowledge Gaps (KGs) to tag questions with one or more types of KGs. We then examine the skew in the distribution of questions for each KG. These new questions can be added to existing VQA datasets to increase the diversity of questions and reduce the skew.
  arXiv  Detail & Related papers  (2020-04-08T00:27:43Z)
• SQuINTing at VQA Models: Introspecting VQA Models with Sub-Questions [66.86887670416193]
  We show that state-of-the-art VQA models have comparable performance in answering perception and reasoning questions, but suffer from consistency problems. To address this shortcoming, we propose an approach called Sub-Question-aware Network Tuning (SQuINT) We show that SQuINT improves model consistency by 5%, also marginally improving performance on the Reasoning questions in VQA, while also displaying better attention maps.
  arXiv  Detail & Related papers  (2020-01-20T01:02:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.