Evidence map›Paper›PMID 36844914›Full record

ArticleApplied intelligence (Dordrecht, Netherlands)2023

A sentiment analysis driven method based on public and personal preferences with correlated attributes to select online doctors.

Jian Wu, Guangyin Zhang, Yumei Xing, Yujia Liu, Zhen Zhang, Yucheng Dong, Enrique Herrera-Viedma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Applied intelligence (Dordrecht, Netherlands), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. The Fears and Hopes of Ukrainian Migrant Workers in Poland in the Pandemic Era.Journal of international migration and integration · 2023
    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.

Jian WuSchool of Economics and Management, Shanghai Maritime University, Shanghai, 201306 China.
Guangyin ZhangSchool of Economics and Management, Shanghai Maritime University, Shanghai, 201306 China.
Yumei XingSchool of Economics and Management, Shanghai Maritime University, Shanghai, 201306 China.
Yujia LiuSchool of Economics and Management, Shanghai Maritime University, Shanghai, 201306 China.ORCID 0000-0002-2275-6648
Zhen ZhangInstitute of Systems Engineering, Dalian University of Technology, Dalian, 116024 China.
Yucheng DongBusiness School, Sichuan University, Chengdu, 610065 China.
Enrique Herrera-ViedmaDepartment of Computer Science and AI, University of Granada, Granada, 18071 Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes a method to assist patients in finding the most appropriate doctor for online medical consultation. To do that, it constructs an online doctor selection decision-making method that considers the correlation attributes, in which the measure of attribute correlation is derived from the history real decision data. To combine public and personal preference with correlated attributes, it proposes a Choquet integral based comprehensive online doctor ranking method. In detail, a two stage classification model based on BERT (Bidirectional Encoder Representations from Transformers) is used to extract service features from unstructured text reviews. Then, 2-additive fuzzy measure is adopted to represent the patient public group aggregated attribute preference. Next, a novel optimization model is proposed to combine the public preference and personal preference. Finally, a case study of dxy.com is carried out to illustrate the procedure of the method. The comparison result between proposed method and other traditional MADM (multi-attribute decision-making) methods prove its rationality.

Indexed as

2-additive fuzzy measureChoquet integralDoctor selectionMulti-attribute decision-makingOnline reviewsSentiment analysis

Identifiers

PMID36844914
PMCPMC9940095

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.