Evidence map›Paper›PMID 31865902›Full record

ArticleBMC bioinformatics2019

Conceiving an application ontology to model patient human papillomavirus vaccine counseling for dialogue management.

Muhammad Amith, Kirk Roberts, Cui Tao

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.0field-weighted citation impact, top 10% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Knowledge Graph and Deep Learning-based Text-to-GQL Model for Intelligent Medical Consultation Chatbot.Information systems frontiers : a journal of research and innovation · 2022
    Article
  6. Dental EHR-infused Persona Ontologies to Enrich Dental Dialogue Interaction of Agents.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021
    Article
  7. Patient-Provider Communication Training Models for Interactive Speech Devices.Digital human modeling and applications in health, safety, ergonomics and risk management : human body, motion and behavior : 12th International Conference, DHM 2021, held as part of the 23rd HCI International Conference, HCII 2021, Vir... · 2021
    Article
  8. Article
  9. Article
  10. Towards an ontology-based medication conversational agent for PrEP and PEP.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2020
    Article
  11. Examining Potential Usability and Health Beliefs Among Young Adults Using a Conversational Agent for HPV Vaccine Counseling.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020
    Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Muhammad AmithSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Road, Suite 600, Houston, TX, 77030, USA.
Kirk RobertsSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Road, Suite 600, Houston, TX, 77030, USA.
Cui TaoSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Road, Suite 600, Houston, TX, 77030, USA. cui.tao@uth.tmc.edu.
The University of Texas Health Science Center at Houston · US

Funding

Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
Patient Medical History Representation, Extraction, and Inference from EHR DataR01LM011829 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TAO, CUI · 2014 to 2018
$1.7M
Natural Language Question Understanding for Electronic Health RecordsR00LM012104 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI ROBERTS, KIRK EDWARD · 2016 to 2018
$747k
NIAID NIH HHS R01 AI130460NLM NIH HHS R00 LM012104NLM NIH HHS R01 LM011829
6 · The paper itself

Abstract

backgroundIn the United States and parts of the world, the human papillomavirus vaccine uptake is below the prescribed coverage rate for the population. Some research have noted that dialogue that communicates the risks and benefits, as well as patient concerns, can improve the uptake levels. In this paper, we introduce an application ontology for health information dialogue called Patient Health Information Dialogue Ontology for patient-level human papillomavirus vaccine counseling and potentially for any health-related counseling.

resultsThe ontology's class level hierarchy is segmented into 4 basic levels - Discussion, Goal, Utterance, and Speech Task. The ontology also defines core low-level utterance interaction for communicating human papillomavirus health information. We discuss the design of the ontology and the execution of the utterance interaction.

conclusionWith an ontology that represents patient-centric dialogue to communicate health information, we have an application-driven model that formalizes the structure for the communication of health information, and a reusable scaffold that can be integrated for software agents. Our next step will to be develop the software engine that will utilize the ontology and automate the dialogue interaction of a software agent.

Indexed as

Papillomavirus VaccinesCounselingFemaleHospitalsHumansPapillomavirus InfectionsSoftwarePapillomavirus VaccinesConversational agentDialogue systemHuman papillomavirus vaccineOntologyPatient provider communication

Identifiers

PMID31865902
PMCPMC6927108
OpenAlexW2995108930

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.