ArticleJournal of the American Medical Informatics Association : JAMIA2020
Consumer health information and question answering: helping consumers find answers to their health-related information needs.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Question Answering for Electronic Health Records: Scoping Review of Datasets and Models.Journal of medical Internet research · 2024Article
- A content analysis of YouTube videos on palliative care: understanding the quality and availability of online resources.Palliative care and social practice · 2024Article
- Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review.Journal of medical Internet research · 2023Article
- From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs.Research square · 2023Article
- From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs.medRxiv : the preprint server for health sciences · 2023Article
- CHQ- SocioEmo: Identifying Social and Emotional Support Needs in Consumer-Health Questions.Scientific data · 2023Article
- quEHRy: a question answering system to query electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Epidemic Question Answering: question generation and entailment for Answer Nugget discovery.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Complex Knowledge Base Question Answering for Intelligent Bridge Management Based on Multi-Task Learning and Cross-Task Constraints.Entropy (Basel, Switzerland) · 2022Article
- Meeting the information and communication needs of health disparate populations.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Automatic question answering for multiple stakeholders, the epidemic question answering dataset.Scientific data · 2022Article
- A conversational agent system for dietary supplements use.BMC medical informatics and decision making · 2022Article
- Strengthening the Case for Universal Health Literacy: The Dispersion of Health Literacy Experiences Across a Southern U.S. State.Health literacy research and practice · 2022Article
- Search like an expert: Reducing expertise disparity using a hybrid neural index for COVID-19 queries.Journal of biomedical informatics · 2022Article
- Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2020Article
- The Unified Medical Language System SPECIALIST Lexicon and Lexical Tools: Development and applications.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Consumer- and patient-oriented informatics innovation: continuing the legacy of Warner V. Slack.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- A question-entailment approach to question answering.BMC bioinformatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectiveConsumers increasingly turn to the internet in search of health-related information; and they want their questions answered with short and precise passages, rather than needing to analyze lists of relevant documents returned by search engines and reading each document to find an answer. We aim to answer consumer health questions with information from reliable sources. MATERIALS AND
methodsWe combine knowledge-based, traditional machine and deep learning approaches to understand consumers' questions and select the best answers from consumer-oriented sources. We evaluate the end-to-end system and its components on simple questions generated in a pilot development of MedlinePlus Alexa skill, as well as the short and long real-life questions submitted to the National Library of Medicine by consumers.
resultsOur system achieves 78.7% mean average precision and 87.9% mean reciprocal rank on simple Alexa questions, and 44.5% mean average precision and 51.6% mean reciprocal rank on real-life questions submitted by National Library of Medicine consumers. DISCUSSION: The ensemble of deep learning, domain knowledge, and traditional approaches recognizes question type and focus well in the simple questions, but it leaves room for improvement on the real-life consumers' questions. Information retrieval approaches alone are sufficient for finding answers to simple Alexa questions. Answering real-life questions, however, benefits from a combination of information retrieval and inference approaches.
conclusionA pilot practical implementation of research needed to help consumers find reliable answers to their health-related questions demonstrates that for most questions the reliable answers exist and can be found automatically with acceptable accuracy.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.