Evidence map›Paper›PMID 37637720›Full record

ArticleJournal of healthcare informatics research2023

Extracting Complementary and Integrative Health Approaches in Electronic Health Records.

Huixue Zhou, Greg Silverman, Zhongran Niu, Jenzi Silverman, Roni Evans, Robin Austin, Rui Zhang

Abstract read
In one paragraph

Article in Journal of healthcare informatics research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Huixue ZhouInstitute for Health Informatics, University of Minnesota, Minneapolis, MN 55414 USA.
Greg SilvermanDepartment of Surgery, University of Minnesota, Minneapolis, MN 55414 USA.
Zhongran NiuInstitute for Health Informatics, University of Minnesota, Minneapolis, MN 55414 USA.
Jenzi SilvermanEarl E. Bakken Center for Spirituality & Healing, University of Minnesota, Minneapolis, MN 55414 USA.
Roni EvansEarl E. Bakken Center for Spirituality & Healing, University of Minnesota, Minneapolis, MN 55414 USA.
Robin AustinSchool of Nursing, University of Minnesota, Minneapolis, MN 55414 USA.
Rui ZhangDepartment of Surgery, University of Minnesota, Minneapolis, MN 55414 USA.

Funding

Detecting synergistic effects of pharmacological and non-pharmacological interventions for AD/ADRDR01AG078154 · NIA · UNIVERSITY OF MINNESOTA · PI HUA XU, RUI ZHANG · 2022 to 2026
$4.2M
A Translational Informatics Framework to Mine Efficacy and Safety of Dietary SupplementsR01AT009457 · NCCIH · UNIVERSITY OF MINNESOTA · PI RUI ZHANG · 2017 to 2026
$4.1M
NCCIH NIH HHS R01 AT009457NIA NIH HHS R01 AG078154
6 · The paper itself

Abstract

Complementary and Integrative Health (CIH) has gained increasing popularity in the past decades. While the evidence bases to support them are growing, there is still a gap in understanding their effects and potential adverse events using real-world data. The overall goal of this study is to represent information pertinent to both psychological and physical CIH approaches (specifically, using examples of music therapy, chiropractic, and aquatic exercise in this study) in an electronic health record (EHR) system. We also aim to evaluate the ability of existing natural language processing (NLP) systems to identify CIH approaches. A total of 300 notes were randomly selected and manually annotated. Annotations were made for

Indexed as

Complementary and Integrative HealthElectronic health recordKnowledge representationNatural language processing

Identifiers

PMID37637720
PMCPMC10449701

What OpenQuestion holds

Textmetadata
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.