Evidence map›Paper›PMID 34574921›Full record

ArticleHealthcare (Basel, Switzerland)2021

Predicting Emotional Valence of People Living with the Human Immunodeficiency Virus Using Daily Voice Clips: A Preliminary Study.

Ray F Lin, Shu-Hsing Cheng, Yung-Ping Liu, Cheng-Pin Chen, Yi-Jyun Wang, Shu-Ying Chang

Open access · goldAbstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 43% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

6 authors at 4 institutions in 1 country.

Ray F LinDepartment of Industrial Engineering and Management, Yuan Ze University, Taoyuan 32003, Taiwan.ORCID 0000-0002-8368-5783
Shu-Hsing ChengDepartment of Infectious Diseases, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan 33004, Taiwan.
Yung-Ping LiuDepartment of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan.
Cheng-Pin ChenDepartment of Infectious Diseases, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan 33004, Taiwan.
Yi-Jyun WangDepartment of Industrial Engineering and Management, Yuan Ze University, Taoyuan 32003, Taiwan.
Shu-Ying ChangDepartment of Infectious Diseases, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan 33004, Taiwan.
Ministry of Health and Welfare · TWYuan Ze University · TWChaoyang University of Technology · TWNational Yang Ming Chiao Tung University · TW

Funding

Ministry of Health and Welfare 10807, 10908, and 11010Ministry of Science and Technology MOST107-2221-E-155 -033 -MY3
6 · The paper itself

Abstract

To detect depression in people living with the human immunodeficiency virus (PLHIV), this preliminary study developed an artificial intelligence (AI) model aimed at discriminating the emotional valence of PLHIV. Sixteen PLHIV recruited from the Taoyuan General Hospital, Ministry of Health and Welfare, participated in this study from 2019 to 2020. A self-developed mobile application (app) was installed on sixteen participants' mobile phones and recorded their daily voice clips and emotional valence values. After data preprocessing of the collected voice clips was conducted, an open-source software, openSMILE, was applied to extract 384 voice features. These features were then tested with statistical methods to screen critical modeling features. Several decision-tree models were built based on various data combinations to test the effectiveness of feature selection methods. The developed model performed very well for individuals who reported an adequate amount of data with widely distributed valence values. The effectiveness of feature selection methods, limitations of collected data, and future research were discussed.

Indexed as

artificial intelligenceclinical diagnosisfeature selectionHIVspeech emotion recognition

Identifiers

PMID34574921
PMCPMC8466484
OpenAlexW3197977610

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.