Retrieving Lab Test Related Questions from Social Q&A Sites by Combining Shallow Features and Deep Representations.

Yu Lu, Xiao Luo, Zhan Zhang, Haoran Ding, Zhe He

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yu LuPace University, New York, NY, USA.

Xiao LuoIndiana University - Purdue University Indianapolis, Indianapolis, IN, USA.

Zhan ZhangPace University, New York, NY, USA.

Haoran DingIndiana University - Purdue University Indianapolis, Indianapolis, IN, USA.

Zhe HeFlorida state University, Tallahassee, FL, USA.

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024

$37.2M

Systematic Analysis of Clinical Study Generalizability Assessment Methods with InformaticsR21AG061431 · NIA · FLORIDA STATE UNIVERSITY · PI BIAN, JIANG, HE, ZHE · 2019 to 2020

$793k

NCATS NIH HHS UL1 TR001427NIA NIH HHS R21 AG061431

6 · The paper itself

Abstract

Patients face challenges in accurately interpreting their lab test results. To fulfill their knowledge gap, patients often turn to online resources, such as Community Question-Answering (CQA) sites, to seek meaningful information and support from their peers. Retrieving the most relevant information to patients' queries is important to help patients understand lab test results. However, few studies investigated the retrieval of lab test-related questions on CQA platforms. To address this research gap, we build and evaluate a system that automatically ranks questions about lab tests based on their similarity to a given question. The system is tested using diabetes-related questions collected from Yahoo! Answers' health section. Experimental results show that the regression-weighted combination of deep representations and shallow features was most effective in the Yahoo! Answers dataset. The proposed system can be extended to medical question retrieval, where questions contain a variety of lab tests.

Indexed as

Patient PortalsComprehensionDatasets as TopicHealth EducationHealth Knowledge, Attitudes, PracticeHumansSurveys and Questionnaires

Identifiers

PMID33936453

PMCPMC8075538

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.