PaniniQA: Enhancing Patient Education Through Interactive Question Answering
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Adarsha S. Bajracharya | Fei Liu | D. Berlowitz | A. Bajracharya | Hongfeng Yu | Yifan Cao | Alok Kapoor | Pengshan Cai | Dakuo Wang | Huixue Zhou | Zonghai Yao | Meghan Reilly | Lingxi Li
[1] Samuel Osebe,et al. UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations? , 2023, CLINICALNLP.
[2] Anitha Kannan,et al. Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue , 2023, ACL.
[3] Rui Wang,et al. Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method , 2023, ACL.
[4] A. McCallum,et al. Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond , 2023, ACL.
[5] Dan Iter,et al. G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment , 2023, EMNLP.
[6] Henrique Pondé de Oliveira Pinto,et al. GPT-4 Technical Report , 2023, 2303.08774.
[7] Quoc V. Le,et al. The Flan Collection: Designing Data and Methods for Effective Instruction Tuning , 2023, ICML.
[8] Hong Yu,et al. Context Variance Evaluation of Pretrained Language Models for Prompt-based Biomedical Knowledge Probing , 2022, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.
[9] Hong Yu,et al. MedJEx: A Medical Jargon Extraction Model with Wiki’s Hyperlink Span and Contextualized Masked Language Model Score , 2022, EMNLP.
[10] Hong Yu,et al. Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context , 2022, AMIA.
[11] Byron C. Wallace,et al. Learning to Ask Like a Physician , 2022, CLINICALNLP.
[12] Fei Liu,et al. Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition , 2022, NAACL.
[13] K. McKeown,et al. Learning to Revise References for Faithful Summarization , 2022, EMNLP.
[14] Andrew M. Dai,et al. PaLM: Scaling Language Modeling with Pathways , 2022, J. Mach. Learn. Res..
[15] Toby Jia-Jun Li,et al. Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension , 2022, ACL.
[16] Ryan J. Lowe,et al. Training language models to follow instructions with human feedback , 2022, NeurIPS.
[17] Alexander M. Rush,et al. Multitask Prompted Training Enables Zero-Shot Task Generalization , 2021, ICLR.
[18] Mujeen Sung,et al. Can Language Models be Biomedical Knowledge Bases? , 2021, EMNLP.
[19] Toby Jia-Jun Li,et al. It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books , 2021, ACL.
[20] Michael S. Bernstein,et al. On the Opportunities and Risks of Foundation Models , 2021, ArXiv.
[21] Artidoro Pagnoni,et al. Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics , 2021, NAACL.
[22] Noémie Elhadad,et al. What’s in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization , 2021, NAACL.
[23] Veselin Stoyanov,et al. Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art , 2020, CLINICALNLP.
[24] Xinliang Frederick Zhang,et al. CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering , 2020, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[25] Jianfeng Gao,et al. Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing , 2020, ACM Trans. Comput. Heal..
[26] Ramón Fernández Astudillo,et al. On the Importance of Diversity in Question Generation for QA , 2020, ACL.
[27] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[28] Peter Szolovits,et al. Entity-Enriched Neural Models for Clinical Question Answering , 2020, BIONLP.
[29] Ryan McDonald,et al. On Faithfulness and Factuality in Abstractive Summarization , 2020, ACL.
[30] Kirk Roberts,et al. Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering , 2020, LREC.
[31] Xiang Yue,et al. Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset , 2020, ACL.
[32] Ramesh Nallapati,et al. Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering , 2020, ACL.
[33] Amy Ward,et al. 12 tips for effective questioning in medical education , 2020, Medical teacher.
[34] Ronan Le Bras,et al. Unsupervised Commonsense Question Answering with Self-Talk , 2020, EMNLP.
[35] Jianfeng Gao,et al. DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation , 2019, ACL.
[36] Jordan L. Boyd-Graber. What Question Answering can Learn from Trivia Nerds , 2019, ACL.
[37] Yizhou Sun,et al. A Comprehensive Typing System for Information Extraction from Clinical Narratives , 2019 .
[38] William W. Cohen,et al. PubMedQA: A Dataset for Biomedical Research Question Answering , 2019, EMNLP.
[39] Jaewoo Kang,et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining , 2019, Bioinform..
[40] Joseph L. Kannry,et al. Challenges optimizing the after visit summary , 2018, Int. J. Medical Informatics.
[41] Kyomin Jung,et al. Improving Neural Question Generation using Answer Separation , 2018, AAAI.
[42] Jian Peng,et al. emrQA: A Large Corpus for Question Answering on Electronic Medical Records , 2018, EMNLP.
[43] M. Rowe,et al. Language Matters: Denying the Existence of the 30-Million-Word Gap Has Serious Consequences. , 2018, Child development.
[44] Sabita Acharya,et al. Towards Generating Personalized Hospitalization Summaries , 2018, NAACL.
[45] Leora I. Horwitz,et al. Predictors for patients understanding reason for hospitalization , 2018, PloS one.
[46] Ming Zhou,et al. Question Generation for Question Answering , 2017, EMNLP.
[47] Xinya Du,et al. Identifying Where to Focus in Reading Comprehension for Neural Question Generation , 2017, EMNLP.
[48] M. Lussier,et al. Communication and patient participation influencing patient recall of treatment discussions , 2016, Health expectations : an international journal of public participation in health care and health policy.
[49] Peter Szolovits,et al. MIMIC-III, a freely accessible critical care database , 2016, Scientific Data.
[50] Sadid A. Hasan,et al. Towards Topic-to-Question Generation , 2015, CL.
[51] Barbara Di Eugenio,et al. PatientNarr: Towards generating patient-centric summaries of hospital stays , 2014, INLG.
[52] Noah A. Smith,et al. Good Question! Statistical Ranking for Question Generation , 2010, NAACL.
[53] Suzanne E. Mol,et al. Added Value of Dialogic Parent–Child Book Readings: A Meta-Analysis , 2008 .
[54] R. Kessels,et al. Patients’ Memory for Medical Information , 2003, Journal of the Royal Society of Medicine.
[55] Harris Wu,et al. Evaluating Web-based Question Answering Systems , 2002, LREC.
[56] Adarsha S. Bajracharya,et al. Generation of Patient After-Visit Summaries to Support Physicians , 2022, COLING.
[57] T. Campion,et al. A Day-to-Day Approach for Automating the Hospital Course Section of the Discharge Summary. , 2022, AMIA ... Annual Symposium proceedings. AMIA Symposium.
[58] Peter Szolovits,et al. emrKBQA: A Clinical Knowledge-Base Question Answering Dataset , 2021, BIONLP.
[59] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[60] Diane G. Schwartz,et al. Barriers, Facilitators, and Solutions to Optimal Patient Portal and Personal Health Record Use: A Systematic Review of the Literature , 2017, AMIA.
[61] Monique Sénéchal,et al. Discussing stories: on how a dialogic reading intervention improves kindergartners' oral narrative construction. , 2011, Journal of experimental child psychology.